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b/reader/content/_index.md index 54b89a9..6caf307 100644 --- a/reader/content/_index.md +++ b/reader/content/_index.md @@ -10,4 +10,8 @@ It is intended to be paired with a development-heavy project to better reinforce The materials have been adapted over several years of use, although each section of the course may use different subsets of the readings and videos. This is a high-level overview of what the course will be about. The materials roughly break down into 6 high-level modules that are spread across the 13 week of standard academic semester at UBC. -Readings and videos are available for most course concepts. \ No newline at end of file +Readings and videos are available for most course concepts. + +## License + +The readings for this course are licensed using [CC-by-SA](https://creativecommons.org/licenses/by-sa/3.0/). However, it is important to note that the deliverable descriptions, code implementing the deliverables, exams, and exam solutions are considered private materials. We go to considerable lengths to make the project an interesting and useful learning experience for this course. This is a great deal of work, and while future students may be tempted by your solutions, posting them does not do them any real favours. Please be considerate with these private materials and not pass them along to others, make your repos public, or post them to other sites online. \ No newline at end of file diff --git a/reader/content/construction/_index.md b/reader/content/construction/_index.md new file mode 100644 index 0000000..af561ec --- /dev/null +++ b/reader/content/construction/_index.md @@ -0,0 +1,12 @@ +--- +archetype: "chapter" +title: "Software Construction" +weight: 6 +--- + +In this chapter, we explore three key aspects of constructing robust and maintainable code: +1. Refactoring: Enhancing code quality by systematically improving existing code without altering its external behavior. +2. Automation: Streamlining repetitive tasks through build automation, continuous integration (CI), and scripting. Automated pipelines ensure consistent, error-free processes. +3. RESTful APIs: Bridging systems using REST principles, including resource-based architecture, HTTP verbs, and statelessness. RESTful APIs facilitate seamless communication between applications. + +These topics empower software engineers to create efficient, maintainable, and interconnected systems. diff --git a/reader/content/construction/automation/_index.md b/reader/content/construction/automation/_index.md new file mode 100644 index 0000000..70c12d6 --- /dev/null +++ b/reader/content/construction/automation/_index.md @@ -0,0 +1,93 @@ +--- +title: "Automation" +weight: 1 +--- + +Our expectations for modern software systems demand delivering a level of velocity that was not possible in the past. Gone are the days when delivering once every 3-5 years was considered acceptable. Being able to deliver changes on a weekly, daily, or minute-by-minute basis allows software teams to more quickly deliver new products to market or provide more timely bug fixes. + +The figure below shows a coarse-grained view of the broad steps required to get a code change deployed in front of a user. While human intervention is required to make every code change, it should be possible to automatically transition from the change in front of a user. The less time this sequence of actions takes (aka the less friction there is in the build process), the quicker the developer can get feedback to refine or roll back their change. Also, the quicker and easier this process is, the more likely developers are to take advantage of it (e.g., if running tests is hard, developers will try not to run them or if deploying is hard developers will avoid deploying fixes that could benefit users unless they are significant 'enough'). + +{{< figure src="automation_feedback.png" alt="API overview" >}} + +Software systems are increasingly large, distributed, and heterogeneous. Successfully building, testing, and deploying them requires coordinating a huge stack of tools, environments, and systems. This requires a high degree of craftsmanship and has historically involved many manual, laborious, time-consuming, and error-prone steps to translate source code into a shippable executable. The goal of automation is to transform this unsustainable way of building software into a process that is: + +* **Repeatable**: The process of building a product should not vary between different versions of the software (modulo minor improvements over time). A unified building process provides a mechanism for building the system the same way, every time with minimal (preferably no) human intervention required. +* **Reliable**: If the build process was repeatable but was subject to many non-determinstic failures it would not have value. Being able to trust that the repeatable process will run to its successful completion is crucial, although naturally test failures are sometimes to be expected and are not considered a build automation reliability failure (unless the tests themselves are flaky). +* **Revertible**: The build process should make it possible to quickly and transparently revert out of any change. If a build is deployed and is found to have an error in production, it should be possible to automatically revert to a prior build in a quick and transparent way. + + + +The gains from automation can be thought of analogously to the gains made when books could be printed with a printing press instead of being written by hand: the setup time is considerable, but the benefits far outweigh these costs in the long run. + +## Automateable units + +Many software building tasks can be automated: + +1. **Source control**: Every software team uses version control to track their source code resources (and other assets including any automation tools themselves). ```git``` and ```hg``` are two commonly used version control systems that provide a reliable way to access any past development state. +1. **Dependency management**: Code is not developed in isolation; all systems are built upon a host of existing libraries and frameworks. Dependency management solutions provide a means to reliably procure software required for the build process. Common tools include ```ant```, ```mvn```, ```npm```, and ```yarn```. +1. **Build tools**: Compiling the code into shippable units (be they libraries or actual executables) is the next step. While these tasks are sometimes easy, they often involve many independent steps that are joined together by build tools like ```make```, ```ant```, or ```gulp```. +1. **Analysis tools**: Many teams want to have stronger control over their codebase than just ensuring the cod is able to be built. Static analysis tools are commonly used to validate every change to ensure that they do not introduce obvious errors, violate the team's coding guidelines, or contravene other project policies. For example, linting tools commonly validate changes to ensure that common mistakes that have shown to lead to defects or that decrease the maintainability of the system are not introduced. Many tools employ security checkers to ensure that security policies (like not checking tokens or passwords into version control) are being followed. Finally, tools like checkstyle are widely used to ensure a project's source code maintains a consistent style which can make it easier for new team members to join, but also makes it easier during code review. +1. **Test tools**: Tests are code too, so enabling them to be written with as little boilerplate code as possible is important for reducing the costs of automated testing. Many unit test tools in particular have been developed to help software engineers write tests and form them into coherent test suites quickly. These tools include ```jUnit```, ```NUnit```, or ```mocha```. +1. **Test runners**: Once test suites have been developed they must be run in a repeatable way. While this can take place on the engineer's development machine, tests are often run on a common infrastructure to increase the consistency between test runs for all developers. Tools like ```Jenkins```, ```Bamboo```, and ```TravisCI``` all work to execute test suites remotely. These first five steps are often called _continuous integration_. +1. **Deployment**: In the online realm software must be deployed once it is built; this field (often referred to as _continuous deployment_) is beyond the scope of this reading. + +Ultimately effective automation will allow a software system to be checked out, configured with the correct dependencies and tools, built, and unit tested with a single command (or a short sequence of commands that can be placed in a simple script). For example, for the project we can simply run: + +``` +yarn install +yarn run configure +yarn run build +yarn run test +``` + +By enforcing each step to be executable on the command line without developer intervention, the entire process can happen automatically in the background. For example, after every push to a project's VCS the whole process can be automatically performed and the results sent to the engineer if any problems are detected. + +## DevOps + +DevOps is a huge space. Here we just mention how automation has enabled many of the advances that enable DevOps to provide value for software teams. DevOps teams take advantage of automation to enable them to quickly deploy (or revert) changes in production. This also enables many different versions of software to be deployed at once. The most common mechanism for this is called the _feature flag_. Feature flags enable DevOps engineers to turn on and off different features for different users dynamically at runtime. + +Feature flags have been extended for several different use cases. The first is for enabling Canary deployments. A new feature can be trialed on small, targeted subsets of users to gather data and ensure that it works in practice before being rolled out to a larger body of users. + +Another use of feature flags is for A/B testing. In this form of testing, different versions of the same feature (or a new version of a feature against the baseline version) are are simultaneously deployed so that the impact of the new version can be compared to the other version. While A/B testing is widely used in industry, care must be taken to ensure that users are treated fairly as most system users do not choose to use a system to be subjects in experiments. + +An extension to automated tools gaining traction in deployed software platforms is chaos tools. These tools intentionally stress a software system to ensure that its failure and mitigation strategies actually work as intended. This kind of analysis was pioneered by Netflix who are famous for their 'Chaos Monkey' that would randomly terminate micro-services _in production_ to ensure that their platform (and clients) would seamlessly recover without any user noticing that this had happened. These advanced tools are valuable due to the complexity of the environments modern systems are deployed within: while a failure strategy might look good on paper, only through runtime experimentation can a team actually validate that it works properly in practice. Without advanced automation though, teams would be unwilling to perform this kind of dramatic action in production without knowing they can quickly and easily bring the system back up to its regular operation. + +## Package Versioning + +As a part of automated build processes, new software versions are constantly being created. Communicating the meaning of these changes in valuable to all consumers of a software package. Additionally, how packages _your_ software depends upon have changed can also have important ramifications for your upgrade decisions. + +Semantic Versioning, often referred to as SemVer, is a versioning scheme that is commonly used to convey meaning about the underlying changes in a release. Semantic version numbers consist of three parts: `MAJOR.MINOR.PATCH`. Each part is an integer that can only be incremented. Leading 0s are not permitted, unless the part is actually 0 (e.g. 1.02.2 is invalid, but 1.0.2 is fine). + +* **MAJOR:** Incrementing the major version indicates that there are incompatible changes in the API which may affect any client of the package. For example, it is not uncommon for a major version change to cause existing clients of a package to fail to compile. + +* **MINOR:** Incrementing the minor version indicates that new features have been added, but these should not affect the compilation (or operation) of existing clients. Minor versions often introduce new APIs. + +* **PATCH:** Incrementing patch versions should never affect any clients and are typically used to distribute bug fixes. + +When the `MAJOR` version is 0, a package is considered pre-release and any version change should be treated as a breaking change. It is common to add a suffix after the `PATCH` version, separated by a `+`, for example `2.12.7+20251123`. These suffixes are often used to capture new builds that do not necessarily even contain changes that reach the threshold of a `PATCH` (e.g., documentation or logging changes). + +### Upgrading and Downgrading Versions + +**Upgrading:** You can safely upgrade the `MINOR` and `PATCH` versions as they are backwards-compatible. Upgrading the `MAJOR` version requires careful consideration as it may introduce breaking changes. In terms of risk, upgrading `PATCH` versions should always be safe and upgrading `MAJOR` versions is frequently not safe. + +**Downgrading:** Downgrading the `PATCH` version is generally safe as it only involves bug fixes. Downgrading `MINOR` versions can be safe, as long as you have not taken a dependency on an API that was added to the `MINOR` version you depend on. +Downgrading to a library with a preceding `MAJOR` version is likely to cause problems. In general, downgrading `MAJOR` and `MINOR` versions carries risk and is generally avoided. + +Upgrade policies are often captured in metadata files for a project. For example, the following three lines describe alternative upgrade paths: + +```json +"parse5": "^7.1.1", +"typescript": "~5.3.2", +"mocha": "10.0.2" +``` + +In this example, the `^` for parse5 specifies that upgrades to `MINOR` and `PATCH` versions are permitted. The `~` for typescript specifies that upgrades to only the `PATCH` field is permitted. The declaration for mocha specifies that only that single version is acceptable. + +## References + +* A humorous discussion of automation can be found in the [Phoenix Project](https://www.amazon.ca/Phoenix-Project-DevOps-Helping-Business/dp/1942788290/). + +* Nice set of [build automation](http://www.slideshare.net/actionjackx/build-automation-101) slides. + diff --git a/reader/content/construction/automation/automation_feedback.png b/reader/content/construction/automation/automation_feedback.png new file mode 100644 index 0000000000000000000000000000000000000000..1ae4c4e5991156b080d587ef702420a62220ebcc GIT binary patch literal 58496 zcmZ5ocOcaN|9`*l?e2`T*D2$YWY4n4DMeCdQIsPiB_n0zaEHh!A}it=Mx}@{O1#Zv zR+3rHN@X32!{K-8v%Ww7xO;oQU*kC*&*$U$dcMV49yQ@)-^LCA0Oui7V`~6lVSWU) zSW(PhO&Ibx^B0SU(Gep6c$Ub)a7Hq}+a+gQ&;>7V zf1MzG$=`SAFh6fyR+hy5zU9(+eM$Qxmbm>s7u|4smG&s9NE)!?a5%k-uI@V4#s~j; zocT>(^6aHczB^+&pI!8y8ko3ZxN-E0# zjLm#hZ|ka##YImy=FD5;8>s62zVm;s{WXuC^48@4So}K;jxng2=b|tK~%zy5ucHq}9 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zt=p~xaK_(L{98uWQF}L#DTU#G3FZgTI^Y$T9e2Y;0hbI{XnAuKJx!$KKUMpGgry8V z(L2>t_$?jqd$aE=pnU-av$@&kO*5Iu5GA`U%Pf@K=K8n(f0y+Gdw{UF0{!RbU?SS( zz@A#Atz1O7j%rsN3;*={#5YHH6vxMpD~xM=1zgF07@Ys%-|?Z&vjdO8{@qdrKY?e> z5qWqdfLn>H6}%fuxN9d@pZCK4pR@jxIutj$8J_X;M9c7n6RcH(7X4;7=BK(_e4icLLD@ImiynEY4E2Likwosz9h&|hr< zf<%XchJ(}5S(ipnFqR;sf?-OHkPc?r-6Z+9gTIIG@O~0UB1!*)i2grcL*UnAaYAGa zlmB0r{#)d)4QIXUtD5k4kpF2v|5@GdUq!yXBhdf8&41b3Z$ + +For example, the code below evolved from being just about printing a value to also computing it. To address this, the developer decided to refactor it so the method has a more specific responsibility and performs an extract method refactoring; before the code looked like this: + +```typescript +public printOwing() { + printBanner(); + let owing = 0; + for (const t of this.tasks) { + owing += t.getValue(); + } + // print details + Log.info("amount: " + owing); +} +``` + +After, a private method ```getOwing``` has been extracted and ```printOwing``` has been simplified to: + +```typescript +public printOwing() { + printBanner(); + // print details + Log.info("amount: " + this.getOwing()); +} + +private getOwing() { + let owing = 0; + for (const t of this.tasks) { + owing += t.getValue(); + } + return owing; +} +``` + +In another instance, a developer realizes upon adding a new feature (```CourseProcessor```) that it shares similar structure to another existing feature and having both complicates the client code. + +```typescript +class RoomsParser { + public parseRooms(id: string, zip: JSZip) { + // ... + } +} + +class CourseProcessor { + public processCourses(id: string, zip: JSZip) { + // ... + } +} +``` + +To simplify the client the developer performs three refactorings. First they perform an extract interface refactoring. Then they add a return type to `parse`, and finally they perform a rename refactoring on `CourseProcessor`: + +```typescript +interface IParser { + parse(id: string zip: JSZip): boolean; +} + +class RoomsParser implements IParser { + public parse(id: string, zip: JSZip): boolean { + // ... + } +} + +class CourseParser implements IParser { + public parse(id: string, zip: JSZip): boolean { + // ... + } +} +``` + +## Code smells + +In order to be useful, software must: + +* Accomplish its purpose. +* Afford change. +* Be understandable. + +Code smells are properties of code that are often symptoms of specific code deficiencies. In particular, smells often make it hard for the code to be understood by future developers, which in turn makes the code harder to evolve and change. Smells often arise as code evolves: as defects are fixed and new features are added, the original design of the code can be obfuscated, and new additions can introduce additional coupling and be less cohesive to the code that was there before. + +Code smells often refer to structural deficiencies within a codebase that makes the code harder to reason about and evolve. While most smells are encountered as structural inhibitors to change, non-structural challenges that make the code hard to understand are also considered smells. Regardless of the source, code smells are often considered one form of technical debt. Smells are never absolute: they are usually informally identified and are sometimes not signs of poor code (e.g., highly generic library and framework code often violate many smells). + +At their core, code smells highlight properties of code that could be detrimental to: + +* Debugging. +* Understanding. +* Quality. + +Specific smells often match up with refactorings that are best able to resolve them; for this reason, smells and refactorings are often discussed in tandem. Some common smells are listed below, along with the _refactorings_ that are often used to resolve them. + +* **Duplicated code**: This represents one of the most common code smells and is identified by code being duplicated throughout the system (these duplicates are often called code clones). If the duplicates take place in the same class, an _extract method_ refactoring can be used to have all copies in the class use the same single implementation. If the code is in related classes, a _push up_ refactoring can move the method into a common supertype. If the duplicate code is in unrelated classes, a more complex analysis is often needed. While clones are usually code smells, some valid reasons for cloning have been identified in the literature, so care is needed when considering how to address these. + +* **Long method**: The single responsibility principle specifies that a method should do one thing and do it well. When methods get too long, it is a sign that feature creep has caused the method to become less focused. While thresholds are hard to set, fitting the code on one screen is a simple rule of thumb (although one that can be legitimately broken). As with duplicate code, _extract method_ refactorings are often used to split up these methods, as could _extract class_ if functionality is to be delegated to another class. Research has shown that shorter methods (e.g., under 24 lines of code) are more amenable to evolution. + +* **Large class**: As with long method, large class captures classes that have grown beyond their original mandate. _Extract class_ and _extract method_ refactorings are often used in these cases. + +* **Long parameter list**: Methods that take large numbers of parameters are hard to understand and are again often symptomatic of code that is doing too much. These methods can either be split, or one can _introduce parameter object_ to better capture and enforce the parameters for the method. + +* **Divergent change**: While working on code it is often observed that many independent changes involve modifying a specific code element; this is known as a divergent change. This signals that one class or method has become much more important than it should have (perhaps has become a god class). Divergent changes are one symptom that a class is taking on more than one responsibility, as such _extract class_ refactorings are frequently used to split a class into two or more independent classes. If the new classes are kinds of the original class, the new classes may be subtypes of the original class, but more commonly the new classes are parts of the original class, enabling delegation to be used instead. + +* **Shotgun surgery**: Opposite to divergent change, shotgun surgery arises when simple coherent changes require broad changes across a system. These are sometimes called delocalized or scattered changes. Such smells are often a symptom of poor cohesion. Shotgun surgery is most commonly addressed by _move method_ and _move field_ refactorings that can move existing functionality into classes where they can be more cohesive. + +* **Feature envy**: This is a broader code smell that occurs when a method is in the wrong class. Like many of the prior smells, it is most commonly resolved with _extract method_ refactorings. Law of Demeter violations commonly signal feature envy smells. The Law of Demeter states that a program component should have limited information about other components in the system. These violations are usually easy to spot and arise when a function 'reaches into' another function to tell it how to behave. For example, if a method called `db.getResults().sort().print()`, not only does it know about `db` but it also needs to know about the return types to `getResults()` and `sort()` in order to call methods on them. The most straight forward way to avoid Law of Demeter violations is to not call methods on objects returned by another method. + +* **Others**: The _middle man_ smell occurs when a method does not do anything itself but just ends up delegating all work to another class. This smell frequently arises after refactorings that move functionality to other methods but the original base method remains (and just calls the new methods, rather than updating all callers). + +Software is built iteratively over a long period of time. Even good past design decisions will need to be updated as requirements change or the environment changes. Smells are just one way to identify such problems, and refactorings are only one way (although a common one) to improve the structure of a system. + +## References + +* Martin Fowler's [technical debt](http://martinfowler.com/bliki/TechnicalDebt.html) discussion. \ No newline at end of file diff --git a/reader/content/construction/rest/REST_network.png b/reader/content/construction/rest/REST_network.png new file mode 100644 index 0000000000000000000000000000000000000000..7f8d3bd52351c5366159cde5fcbedcc3706419ab GIT binary patch literal 128216 zcmdSBg;!Kx^f!!vFsPI$2uLG{boT(#QVK|S4M+|hLkUun21p}~v~+h#42^VmHv=4`?KQ``c_#M_W{`hG&D3^c{wRnG&J-9;I#_E z0*;IZ8%+Xlm=Fmi2{g3QsE60a_kr)!rgExEXlR}cXlQ+S{@jo7tO~v$@+k09T-)iMR^^A8pN@jj7#j zZS0(c+(l{c&JY4V-yUYCrM^4G*;kgAl- zf0qN_L}@LZogIYO+1=dS*xb0;>>VNOoPvUa>>OO|TwJWc8LUnocFxA`taeUx{|5P= zaiq+hOdYKpoUQEbsBgzLHnDec7Nw=Vo#=o6{kub2i|HsYzTj||VV5#5-BJBTLY~Tlq zt&G}eXyRz{QWEO!_jVevzfnwhtZO5MXdm}s{zxEbL5nuRyoVK&_VV4A7rm7QW?nq8 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zw(<@shel?jv$zTt7%bE5cXf3+-#|z%OJ0)@@^M04s3M`f4k+@B{7s$}Qa~Ue5D-`b1aND+O!?=OAR22|dB?z>rpO-2>M;Rr80=Y!FOHnPViMPx`11JtA8M1{;W37g06R;nF52?m7 zD$Vz6t+%_5 zjg@+>T->-}gA1+6QrTMF)h*1zq-QS=k%@8Hr-SHNmB!Dr zU!(hylB3AQ#~ypkK61+~_W0wEtEByt^4pa6au8B+5jd~4id%1}Eg%pOSUm_}iD9X4 zlm+}`Mo0U;4?HWF;Vqy)71PciSnneh((kP;bJI9vml*3w$GVuz9;YY~!p+kr4 zgCG2$?b@}An9R2+zgu}&Ii!Fr<~CJ5bSD^W(fC}zw#CP%x6BsWa-nY zpP2Q?#t^q&AS^`+#Ca)D4RrqinF=BvA$^ z6%+(pvbV%||7Q6!_o(b2DF3Z;NCAO>KtQ0>5nz=c zz8zH<4V*j_L3Z*GnW%HR;QW~}LhRqaA6Jdiq2uzCr zatEb=a)2Px# literal 0 HcmV?d00001 diff --git a/reader/content/construction/rest/_index.md b/reader/content/construction/rest/_index.md new file mode 100644 index 0000000..9b79615 --- /dev/null +++ b/reader/content/construction/rest/_index.md @@ -0,0 +1,188 @@ +--- +title: "REST" +weight: 3 +--- + +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) is an architectural style heavily used in web-based systems for providing services between different systems (and interfaces). REST defines a means of connecting systems together that focuses on their interfaces rather than their implementations using self-descriptive messages. In this way, REST provides a language to define a specific kind of [APIs](API.md) for communicating across a network. REST services are transported over the network using the [HTTP protocol](https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol). + +{{< figure src="REST_network.png" alt="REST topology" >}} + +REST services define their resources using uniform resource identifiers (URIs). URIs differ from URLs in that a URI does not define its network location and access mechanism (e.g., ```/dataset``` is a URI while ```http://foo.com/dataset``` is a URL). + +Additional data can be sent to a REST endpoint in the form of HTTP headers or in the HTTP body. It is idiomatic for headers to not contain any information about the location of the resource (this should be in the URI) or to carry any data about the request (this should be in the body); headers are mainly for sending metadata (e.g., to request the response data format or encoding or to provide authorization credentials). + +## Verbs + +REST describes how HTTP verbs should be used when communicating with REST services. REST services respond with payloads (typically JSON or XML) and an integer-based response code. Each REST request uses only one verb and receives only one response. + +* ```GET``` Used to read a resource. Get requests should never modify data on a server, meaning that calling them repeatedly should have no visible effects on the server (that is, the request is idempotent). GET requests can have a body but this is not idiomatic; requested resources are defined by the URI alone. Common response codes are 200 (ok) and 404 (not found). + +* ```PUT``` Used to update a resource at a known URI. That said, PUT can also be used to create a resource if the client is allowed to define a new URI that does not already exist. Response bodies are not typically returned for these requests. PUT modifies server resources but in the same way for every request and can be considered idempotent. 200 (ok), 204 (no content), and 404 (not found) are common response codes. + +* ```POST``` Used to create a new resource. The response body should contain a URI to the newly-created resource. POST modifies server resources and is not idempotent. Typical response codes are 201 (created), 404 (not found), and 409 (conflict). + +* ```DELETE``` Removes a resource from the server. Response body is typically not emitted. Although response codes will differ on subsequent requests, the resources on the server will be unchanged so this verb is idempotent. Typical response code is 200 (ok) or 404 (not found). + +* ```PATCH``` Used to modify (not completely replace or delete) a resource. Not idempotent. This is the least-frequently used verb. Typical response codes are 200 (ok), 204 (no content), 404 (not found). + +A simple set of [quick tips](http://www.restapitutorial.com/lessons/restquicktips.html) and [http status codes](httpstatuses.com) can be helpful for your reference. + +REST services almost universally respond to requests with XML or JSON requests. XML documents are more verbose, harder to read and write by hand, and are more difficult to parse. + +```xml + + + 200 + +``` + +In contrast, JSON documents are easier to read and write, are less verbose, and are easier to parse but are not amenable to comments which can make them harder to document (in a way that is still a valid JSON document). + +```json +{ + "code": 200 +} +``` + +The pushback against XML can be more clearly understood when looking at a SOAP envelope, one of the predominant forms of communicating between network services when JSON was developed: + +```xml + + + + remote + + + + + +``` + +## Statelessness + +REST services are often viewed as highly scalable. The main reason for this is that there is an expectation that the client maintains state instead of the server. This means that a client can interact with several different servers for a single set of requests (e.g., if one server gets overloaded requests can be transparently migrated). + +As a concrete example, if a client is trying to undertake a complex multi-step operation, it is the client's responsibility to keep track of where they are, not the server's responsibility. + +Servers are responsible for sending back detailed information about the resources they are returning so that clients have the ability to make further requests of the server with the correct URIs. For example, while could might expect the GitHub ```POST /user/:repo``` endpoint to simply return ```{success: true}``` if a repository was successfully created, it instead returns a wide variety of information that can be used by the user to construct any subsequent requests: + +```json +Status: 201 Created +Location: https://api.github.com/repos/octocat/Hello-World +{ + "id": 1296269, + "owner": { + "login": "octocat", + "id": 1, + "avatar_url": "https://github.com/images/error/octocat_happy.gif", + "gravatar_id": "", + "url": "https://api.github.com/users/octocat", + "html_url": "https://github.com/octocat", + "followers_url": "https://api.github.com/users/octocat/followers", + "following_url": "https://api.github.com/users/octocat/following{/other_user}", + "gists_url": "https://api.github.com/users/octocat/gists{/gist_id}", + "starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}", + "subscriptions_url": "https://api.github.com/users/octocat/subscriptions", + "organizations_url": "https://api.github.com/users/octocat/orgs", + "repos_url": "https://api.github.com/users/octocat/repos", + "events_url": "https://api.github.com/users/octocat/events{/privacy}", + "received_events_url": "https://api.github.com/users/octocat/received_events", + "type": "User", + "site_admin": false + }, + "name": "Hello-World", + "full_name": "octocat/Hello-World", + "description": "This your first repo!", + "private": false, + "fork": true, + "url": "https://api.github.com/repos/octocat/Hello-World", + "html_url": "https://github.com/octocat/Hello-World", + "archive_url": "http://api.github.com/repos/octocat/Hello-World/{archive_format}{/ref}", + "assignees_url": "http://api.github.com/repos/octocat/Hello-World/assignees{/user}", + "blobs_url": "http://api.github.com/repos/octocat/Hello-World/git/blobs{/sha}", + "branches_url": "http://api.github.com/repos/octocat/Hello-World/branches{/branch}", + "clone_url": "https://github.com/octocat/Hello-World.git", + "collaborators_url": "http://api.github.com/repos/octocat/Hello-World/collaborators{/collaborator}", + "comments_url": "http://api.github.com/repos/octocat/Hello-World/comments{/number}", + "commits_url": "http://api.github.com/repos/octocat/Hello-World/commits{/sha}", + "compare_url": "http://api.github.com/repos/octocat/Hello-World/compare/{base}...{head}", + "contents_url": "http://api.github.com/repos/octocat/Hello-World/contents/{+path}", + "contributors_url": "http://api.github.com/repos/octocat/Hello-World/contributors", + "deployments_url": "http://api.github.com/repos/octocat/Hello-World/deployments", + "downloads_url": "http://api.github.com/repos/octocat/Hello-World/downloads", + "events_url": "http://api.github.com/repos/octocat/Hello-World/events", + "forks_url": "http://api.github.com/repos/octocat/Hello-World/forks", + "git_commits_url": "http://api.github.com/repos/octocat/Hello-World/git/commits{/sha}", + "git_refs_url": "http://api.github.com/repos/octocat/Hello-World/git/refs{/sha}", + "git_tags_url": "http://api.github.com/repos/octocat/Hello-World/git/tags{/sha}", + "git_url": "git:github.com/octocat/Hello-World.git", + "hooks_url": "http://api.github.com/repos/octocat/Hello-World/hooks", + "issue_comment_url": "http://api.github.com/repos/octocat/Hello-World/issues/comments{/number}", + "issue_events_url": "http://api.github.com/repos/octocat/Hello-World/issues/events{/number}", + "issues_url": "http://api.github.com/repos/octocat/Hello-World/issues{/number}", + "keys_url": "http://api.github.com/repos/octocat/Hello-World/keys{/key_id}", + "labels_url": "http://api.github.com/repos/octocat/Hello-World/labels{/name}", + "languages_url": "http://api.github.com/repos/octocat/Hello-World/languages", + "merges_url": "http://api.github.com/repos/octocat/Hello-World/merges", + "milestones_url": "http://api.github.com/repos/octocat/Hello-World/milestones{/number}", + "mirror_url": "git:git.example.com/octocat/Hello-World", + "notifications_url": "http://api.github.com/repos/octocat/Hello-World/notifications{?since, all, participating}", + "pulls_url": "http://api.github.com/repos/octocat/Hello-World/pulls{/number}", + "releases_url": "http://api.github.com/repos/octocat/Hello-World/releases{/id}", + "ssh_url": "git@github.com:octocat/Hello-World.git", + "stargazers_url": "http://api.github.com/repos/octocat/Hello-World/stargazers", + "statuses_url": "http://api.github.com/repos/octocat/Hello-World/statuses/{sha}", + "subscribers_url": "http://api.github.com/repos/octocat/Hello-World/subscribers", + "subscription_url": "http://api.github.com/repos/octocat/Hello-World/subscription", + "svn_url": "https://svn.github.com/octocat/Hello-World", + "tags_url": "http://api.github.com/repos/octocat/Hello-World/tags", + "teams_url": "http://api.github.com/repos/octocat/Hello-World/teams", + "trees_url": "http://api.github.com/repos/octocat/Hello-World/git/trees{/sha}", + "homepage": "https://github.com", + "language": null, + "forks_count": 9, + "stargazers_count": 80, + "watchers_count": 80, + "size": 108, + "default_branch": "master", + "open_issues_count": 0, + "has_issues": true, + "has_wiki": true, + "has_pages": false, + "has_downloads": true, + "pushed_at": "2011-01-26T19:06:43Z", + "created_at": "2011-01-26T19:01:12Z", + "updated_at": "2011-01-26T19:14:43Z", + "permissions": { + "admin": false, + "push": false, + "pull": true + } +} +``` + +Another example of this is in terms of pagination. Instead of expecting there to be a REST endpoint called ```GET /:query/next``` which would require the server to keep track of the next result for every client, in REST-based systems a more appropriate endpoint would be ```GET /:query/:rowNum``` where the client would instead track which row they wanted to see next. + +## Design goals + +There are several orthogonal ideas to keep in mind when designing REST APIs. + +The first is that the API must provide a _valuable_ service. Without this value, the API will not be widely used, no matter how well it is designed. Value can be derived in many ways be it through access to data (like a weather API), access to a piece of functionality (like a machine learning API), or access to an audience (like a social network API). + +APIs should also be sufficiently _flexible_ to work in a wide variety of circumstances. This might mean providing multiple data formats, supporting different protocols, or versions. Additionally, enabling batch or update operations can also ease common developer tasks that might otherwise be hard to perform. This often involves considering the bootstrap process and how long it takes a developer from visiting an API document to successfully making a query (often referred to as the time-to-hello-world). + +Effectively _measuring_ your APIs is also important for understanding how your customers are using your system. When an API is servicing hundreds of thousands of requests per day, it is important to understand how your endpoints are being used and how they are generating errors. These measurements are useful both to the API developer to monitor the API, but also for API clients so they can check on the API if they are having any difficulties. Many API platforms have status pages where you can check how the [platform](https://api.twitterstat.us/) [is](https://status.stripe.com/) [performing](https://status.github.com/). + +Finally, effective API _documentation_ is key for helping developers understand how to use any API. These documents should clearly describe what the API does, what parameters it expects, and the structure of its return document. Concrete examples showing how the API can be used are also extremely helpful in this context. Many [great](https://developer.github.com/v3/) [examples](https://strava.github.io/api/) can be found online demonstrating effective REST API documentation. + + + + diff --git a/reader/content/ethics-security/_index.md b/reader/content/ethics-security/_index.md new file mode 100644 index 0000000..583e803 --- /dev/null +++ b/reader/content/ethics-security/_index.md @@ -0,0 +1,8 @@ +--- +archetype: "chapter" +title: "Ethics and Security" +weight: 7 +--- + +In the ever-evolving landscape of software engineering, ethical considerations, robust security practices, and respect for intellectual property rights are paramount. +As third-year students in this field, you’ll delve into these interconnected realms, understanding their impact on software development, user trust, and societal well-being. \ No newline at end of file diff --git a/reader/content/ethics-security/ethics/_index.md b/reader/content/ethics-security/ethics/_index.md new file mode 100644 index 0000000..d3a04e3 --- /dev/null +++ b/reader/content/ethics-security/ethics/_index.md @@ -0,0 +1,57 @@ +--- +title: "Ethics" +weight: 2 +--- + +Due to the breadth and impact of software, it is unsurprising that the decisions software engineers make frequently require careful ethical consideration. The field of ethics involves the study of moral principles and values. Ethics intersects with software engineering across the product lifecycle including during requirements analysis, system design, implementation, validation, and after deployment. + +At a high-level, some common ways software engineering considerations intersect ethical considerations include: + +* ***Trust.*** Software systems are incredibly complex and have wide-ranging societal impact. Given the critical roles software systems play in society, whether in medical devices, transportation, or finance, users need to be able to trust that systems are secure and trustworthy. Software engineers must carefully consider the potential consequences of their systems to consider whether they may violate ethical values. + +* ***Defects.*** Given the impact software has on modern life, software reliability can have clear impacts on the safety of people or groups. During design, development, and validation decisions can be made that prioritize schedule and cost over the impact defects may have on users. + +* ***Data.*** Software systems can collect and aggregate large volumes of data on individuals. These data can include names, demographic information, health records, financial records, and information about online activities. Collecting these data may be appealing from a business-perspective, but must be within the framework of both the law and the consent solicited from users. Privacy-preservation is often more challenging than is appreciated at a high-level, especially when motivated groups seek to break privacy protections. + +* ***IP.*** Software systems are the byproduct of the (often expensive) expenditure of intellectual effort. To protect the investments of these efforts, individuals and companies rely on intellectual property mechanisms to protect their intellectual efforts. More details on these mechanisms can be found in the [IP reading](IP.md). + +Ultimately, the impact software engineers can have on society requires that engineers also carefully consider the ethical ramifications of their actions. + +## Professionalism + +Professional guidelines have been developed to provide guidance to software engineers to help them understand the scope of their professional responsibilities, which include ethical concerns. The [ACM code of ethics](https://www.acm.org/code-of-ethics) is one such set of guidelines. While research [has shown](https://jssmith1.github.io/assets/pdf/FSE18_NIER.pdf) that reading the ACM code of ethics alone might not induce the consideration one might hope, the document is still a valuable source that details the diversity of professional concerns facing software engineers. In this way, the code acts as a mechanism to reduce the 'unknown unknowns' by casting light on the ethical responsibilities software engineers have. The ACM code is broken down into three broad sections which reflect the breadth of concerns software engineers are responsible for considering. + +The first section, *general principles*, details the high-level ethical guidelines software engineers should embody as moral members of society. This section lays out the foundational principles upon which the second and third section build. The principles acknowledge the impact software engineers can have and how their work can shape different members of society in unique ways. While it is natural to believe that software engineers should avoid harm, the guidelines also clarify that unintended harms must also be mitigated and undone, once they are discovered. Given the complex nature of the field, honesty and fairness are also intrinsic properties that should underly all work software engineers perform. Finally, the importance of privacy and confidentiality, both for individuals and organizations, are described to reenforce their central importance for ethical decision making. + +The second section, *professional responsibilities*, describes how software engineers should work in a professional team-based setting to accomplish the guidelines outlined in the first section. The final section, *professional leadership responsibilities*, outlines the additional responsibilities incumbent on those in leadership roles within technical organizations. These roles include not just traditional managers, but all members of teams that have influence over technical decision making. + +## Ethical decision making + +The trolley problem is a thought experiment that can be used to reason about ethical situations. In the trolley problem, a person is faced with a situation where a trolley is rolling down a track and will result in the death of multiple people, or the person can divert the train onto a different track, but will result in the death of a single person. + +This thought experiment is relevant to software engineering as it demonstrates how both action, and inaction, can lead to harm in safety-critical situations. For example, imagine a software engineer working on self-driving car software. One could imagine developing a policy that will strive to protect the safety of the occupants of the car, but how will the car react when put in a situation where it must balance the occupant's safety with those of a single pedestrian or bicyclist? Similarly, how will those decisions differ if the occupants safety must be balanced with a school bus or ambulance working on the side of the road? In these situations, engineers must consider the ethical implications of their design choices acknowledging that their decisions will have implications both for those who accept the technology and the team the build it (e.g., the car buyers) as well as those who have not (e.g., the bicyclist or first responders). + +Along another dimension of harm, software systems have a wide surface upon which to expose discrimination that reenforces biases that can disadvantage different groups of people based on characteristics that are legally protected. These can be obviously impactful systems, for example systems that evaluate [parole eligibility](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing), or more inconspicuous systems such as those that use facial recognition to highlight important [parts of images](https://algorithmwatch.org/en/google-vision-racism/). + +These concerns have grown in importance in recent years through the increased reliance on machine learning models, the visibility into which is less obvious for technical teams than with prior hard-coded or heuristic-based approaches. Detecting and combating bias in machine learning-based systems has led to the emergence of [explainable AI](https://insights.sei.cmu.edu/blog/what-is-explainable-ai/) as a field of increased focus. + +While most software engineers will not contribute to self-driving vehicle platforms, the expanding applications of software and their entanglement with society increases the likelihood that systems will be deployed in safety-critical situations. Additionally, almost all software engineers will develop systems that end up providing information to users, and thus the biases and discrimination that those systems confer must be carefully identified and controlled. + + + + + + diff --git a/reader/content/ethics-security/ip/_index.md b/reader/content/ethics-security/ip/_index.md new file mode 100644 index 0000000..3f43f6e --- /dev/null +++ b/reader/content/ethics-security/ip/_index.md @@ -0,0 +1,45 @@ +--- +title: "Intellectual Property" +weight: 3 +--- + + +All tech-based organizations generate and consume intellectual property (IP). IP is protected under a variety of mechanisms to both enable organizations to protect and profit from their IP. + +## Mechanisms + +The most common mechanisms used for protecting intellectual property are: + +***Trademarks*** are used to differentiate products or organizations within the marketplace. Trademarks are registered on a country-by-country basis and cost a few thousand dollars / country to register. These can be renewed indefinitely. In the tech space, companies have tried unsuccessfully to register the 'look and feel' of an application as a Trademark (Apple v. Samsung). + +***Copyright*** can be applied to creative works to protect the _representation_ of the work. That is, copyright does not protect the idea, but it protects how that idea is encoded. In the context of software, copyright is commonly used to protect the text of the program (e.g., the source code) but does not protect what the program actually does. Copyright in Canada lasts until the death of the author, plus 50 years at which point the representation enters the public domain. + +***Trade secrets*** are a mechanism to protect information by keeping it from being publicly disclosed. These are commonly protected using confidentiality agreements and can last for as long as the secret itself is protected from disclosure. Programs are often protected as trade secrets, and it has been established that releasing the compiled version of a program is not considered disclosure of the underlying source code. + +***Patents*** are state-conferred specific rights that are used to protect inventions. These provide the inventor the exclusive rights to profit from an invention for a set period of time. Inventions must be novel, non-obvious, and useful in order to be eligible for patenting. + +Patenting software is challenging: software is considered an *abstract idea* that cannot usually be patented. To be eligible for protection, the inventor needs to demonstrate that the software being patented represents a specific novel implementation of the invention, rather than a straightforward application of the abstract idea. Additionally, the provisions that an invention be novel and non-obvious are often difficult to establish for software-based systems as it can be hard to demonstrate these core properties. + +Establishing patents is a long and expensive process, as demonstrating novelty against all prior patents and prior disclosed work and non-obviousness requires the support of IP professionals. In Canada, patent protection lasts for 20 years and is granted by the Canadian Intellectual Property Office. + +Software-based patents are not without controversy: while some proponents argue that patenting allows innovators to protect their expensive intellectual investments, and are therefore incentivized to make those investments. Detractors believe that patents in such a fast-moving field can induce advantages that cannot be overcome in the long term as by the time the patent has expired, the patentors have established themselves such that they cannot be competed with. Balancing these two concerns is a central concern for all IP legislation. + +***Licenses*** are used in conjunction with the options above to enable IP to be monetized without releasing ownership of the underlying protected material. + +At the same time, IP regulations also try to consider the public good. This is because while IP can be used to incentivize the creation of new work by enabling it to drive monetary gain, overly restrictive IP regimes can inhibit the creation of new innovations by making it impractical to enable others to create derivative work that extends previously-existing protected IP. + +## Licenses + +Licenses are widely applied in the software ecosystem to define how software products can be used. All source code is protected by copyright: licenses clarify how the code can be used and whether it can be modified and distributed by others. Below we concentrate on a few popular Open Source licenses and their specific properties. + +***GPLv3*** The General Public License (GPLv3) has been designed to enable software to be used, shared, and modified. This is a *copyleft* license which means that the license must be maintained with the code is modified and/or distributed. Additionally, projects that use GPLv3 code cannot us a less-restrictive license on their own product. + +***LGPLv3*** The Lesser General Public License (LGPLv3), while still a copyleft license, is a more flexible license (than GPLv3). While it requires that changes to code licensed under LGPLv2 to be shared, when used as a library by a product the product itself need not be shared. LGPLv3 allows that software using LGPL libraries can be shared under a more restrictive license than the libraries it uses. + +***MPLv2*** The Mozilla Public License (MPLv2) is another copyleft license that aims for increased flexibility over the LGPLv3 in that it only mandates changes to _files_ licensed MPL to be released (in contrast to the GPL and LGPL which require changes to the _library_ to be released). + +***CC-by*** Several flavours of CC-by licenses have been created by the [Creative Commons](https://creativecommons.org/choose/) to allow individuals and organizations to choose from a set of standardized licenses that can apply in a variety of situations. These licenses tend to be succinct and aim to make it easier to license work in a way that enables others to use, extend, and share the licensed work. For instance, this reading is licensed [CC-by-SA](https://creativecommons.org/licenses/by-sa/3.0/), meaning it can be shared and adapted by others, but that attribution must be provided and that shared versions of this reading must be under the same license. A less restrictive license like [CC-by](https://creativecommons.org/licenses/by/4.0/) would allow for this reading to be modified and redistributed, but would not impose the restriction on the license applied of the modified version of the reading, but I believe it is important that derivative versions should be available for others to learn from as well. + +## References + +* [Do Software Developers Understand Open Source Licenses? ICPC 2017](https://www.cs.ubc.ca/labs/spl/projects/softwarelicensing/resources/2017_ICPC_DoSoftwareDevelopersUnderstandOpenSourceLicenses_preprint.pdf) \ No newline at end of file diff --git a/reader/content/ethics-security/security/_index.md b/reader/content/ethics-security/security/_index.md new file mode 100644 index 0000000..cc54e52 --- /dev/null +++ b/reader/content/ethics-security/security/_index.md @@ -0,0 +1,111 @@ +--- +title: "Security" +weight: 1 +--- + +Software security is a pervasive cross-cutting design concern of grave importance to modern software systems. Security concerns are not just about bad actors but also include unlucky users, untrained users, or malicious insiders. While there are obvious monetary costs associated with security (for instance through money being stolen directly), personnel costs, customer loss, and legal violations also have important indirect costs. Additionally, reputation, privacy, and customer satisfaction can all be influenced by the security of a system. This reading provides only the briefest of glimpses into the field of security. + +> Security is ultimately concerned with managing risk whereby the risks are balanced against the costs associated with securing the system. + +Security comprises a huge space including physical security (e.g., key loggers), system security (e.g., denial of service), network security (e.g., man-in-the-middle), and human security (e.g., social engineering). Assets represent the information or systems that is being secured. The subjects of the system are the people who use, administer, and may try to infiltrate or undermine the system. Policies are required to understand which subjects are allowed to access which assets. Threats represent the reason we need policies (e.g., the existence of unauthorized subjects accessing the wrong assets through software vulnerabilities). + +The DREAD model provides one way to think about security risks: + +``` +risk = +damage (1..10) + +reproducibility (1..10) + +exploitability (1..10) + +# users (1..10) + +discoverability (1..10) +``` + +Four high-level requirements pervade most software security discussions: + +* ***Confidentiality***: Preventing unauthorized parties from accessing (or even knowing about) secured data. +* ***Integrity***: Ensuring that only authorized parties can manipulate data, and only in an approved manner. +* ***Availability***: Resources should always be accessible by authorized users on appropriate occasions. +* ***Accountability***: It should be possible to know how subjects interact with sensitive systems and data. + +In a security context, _assets_ are the systems and data being secured. _Subjects_ are the actors, both legitimate and otherwise, who are interacting with the system and data. The _policies_ are the rules associated with the system; these are key for determining which subjects are permitted to view, interact with, or modify which assets. The _threats_ to the system are the violations of the policies that can be employed to break the security of the system. + +There are four high-level steps involved in securing systems: + +1. ***Understanding***: Before we can secure a system we need to understand the system and its context. + +1. ***Analysis***: The longest phase involves taking our understanding of the system and building meaningful threat models to understand how and why a subject would want to violate the system's security policies. + +1. ***Mitigation***: Once this understanding has been built, the work of actually securing the system begins. At its simplest, this involves reducing the amount of system surface available to unauthorized parties while maintaining authorized user access. + +1. ***Validation***: Finally, testing and security reviews are crucial to check the mitigation steps have been successful. + +There are various kinds (personas) of unauthorized users, all of whom may have different motivations for compromising a system. + +* Accidental attackers. +* Disgruntled employees. +* Script kiddies. +* Curious attackers. +* Automated malware. +* Motivated attackers. +* Hacktivists. +* Cyber terrorists. +* State actors. + +Each of these personas will have different motivations, goals, tools, and experience available to them. In particular, approaches further down the list will have the expertise and resources to exploit much more challenging system vulnerabilities. The [STRIDE](https://msdn.microsoft.com/en-us/library/ee823878(v=cs.20).aspx) threat model was developed at Microsoft to characterize the kinds of vectors these personas may employ to gain access to the system. + +* ***S***poofing: pretend to be another subject. +* ***T***ampering: modifying data. +* ***R***epudiation: denying performing a task. +* ***I***nformation disclosure: access private data. +* ***D***enial of service: halt service. +* ***E***levation of privilege: gaining unauthorized rights. + +## Security principles + +Several security principles have been developed to help mitigate some of these vectors (this is an incredibly incomplete list): + +* ***Defence in depth***: A small violation should not easily lead to a big one. This means that security should not only be implemented at the perimeter of the system and internal controls should still be present. Varying internal security measures can further help avoiding failure propagation. As with most security countermeasures this negatively impacts system complexity and can decrease system usability (as is true for many of these principles). + +* ***Least privilege***: Coarse-grained privileges allow both malicious and accidental access to unauthorized data. Implementing roles and access control lists are one of the most common ways of avoiding these exploits, although this only really works once the subjects, assets, and policies for the system has been carefully specified. + +* ***Separation of duties***: Sensitive or critical actions in a secure system should not be left to a single user. This applies to both authorized (to avoid accidents) and unauthorized parties (to avoid broad exploits). This kind of separation is often apparent in accounting systems whereby actions must be approved by second or third parties to ensure compliance and correctness. + +* ***Strong authentication***: Making it hard for unauthorized users to gain access to the system can be enhanced using strong passwords or some kind of two-factor (or multi-factor) authentication. In two-factor authentication, users must both know something (like their password) and have something (like a phone, chip-with-pin card, or number-generating fob). This makes it so if only one part of the mechanism was lost (for instance the password written on a post-it note) the system could not be directly breached. + +* ***Non-repudiation***: System users must be held accountable. Non-repudiation means auditing system behaviour to track how the system is being used. Auditing is an essential tool for tracking the extent to which unauthorized parties have accessed the system and what they have done. It is also useful for ensuring that authorized parties know their usage of the system is being traced. One downside of this approach is that audit logs themselves can contain sensitive information (or point to it) and must themselves be carefully secured. + + +## Mitigation strategies + +After a system has been modelled and its threats understood, we can start to design mitigation strategies. Rather than step through an abstract list of strategies, here we will discuss more concretely the kinds of steps Google undertakes. These are covered in a great [white paper](https://cloud.google.com/security/security-design/) if you want additional detail. + + + +Google's security strategy revolves around providing a secure infrastructure for all of their services to use. These range from low-level hardware support to high-level operational support. Their mitigation strategies can be split into five key levels: + +* Hardware infrastructure: In-house designs for server and network equipment to reduce risk of unknown hardware running in secure facilities. +* Service deployment: All services communicate using encrypted links. Services can only interact with other services with which they are authorized. Services can also only read/write data they are authorized to access. Services run in independent hypervisors although some critical services are also run on completely independent machines. +* User identity: All Google employees use multi-factor authentication internally. Employees are also granted restricted fine-grained access to they services and data they are allowed to access. +* Secure data storage: All data is encrypted with rotating key encryption. Data remanence (e.g., deletion) is also carefully managed, although there are challenges given the distributed nature of the data. +* Internet communication: All front-end services go through the GoogleFrontEnd (GFE) that ensures that perfect forward security is correctly applied and that all certificates are correctly configured. The GFE also has the ability to mitigate DoS attacks by throttling or balancing connections. +* Operational security: Engineers practice safe coding practices (such as security code reviews, using static and dynamic analyses, and employing security-aware frameworks and libraries). Google also works with the OSS projects that its tech stack relies on to find vulnerabilities in code they are using that could compromise their own services. + +At their core, these mitigation strategies are a great example of defence in depth (multiple layers of countermeasures that does not solely rely on border security), least privilege (employees are strictly limited in the services and data they can access, as are the services themselves from one another), strong authentication (universal 2FA authentication that not only grants access to systems but is passed along with individual service calls), and non-repudiation (extensive logging and unusual activity detection). + +## References + +* [OWASP](https://www.owasp.org) + +* [Web application](http://martinfowler.com/articles/web-security-basics.html#ProtectUserSessions) discussion. + +* [Secure by design](http://www.slideshare.net/EoinWoods1/secure-by-design-security-design-principles-for-the-rest-of-us) Eoin Woods GOTO slides. + +* OWASP [threat modelling](https://www.owasp.org/index.php/Threat_Risk_Modeling) wiki page. \ No newline at end of file diff --git a/reader/content/high-level-design/_index.md b/reader/content/high-level-design/_index.md new file mode 100644 index 0000000..a930173 --- /dev/null +++ b/reader/content/high-level-design/_index.md @@ -0,0 +1,121 @@ +--- +archetype: "chapter" +weight: 4 +title: "High-Level Design" +--- + +Software design is a key translational step in the software development process. During design, requirements are transformed into a format that can be implemented directly. More concretely, the input to the design process is a list of requirements and the output is a set of classes, methods, and fields that must be implemented. + +The design process is highly iterative, but can often be thought of as two broad activities: + +* **High Level Design:** During high level design the key architecture, abstractions, and relationships required to build the system are identified. This often involves evaluating different ways the system might need to evolve in the future and evaluating the relative merits of alternative architectural decisions. The decisions made in high level design guides the low level design process. Many design principles and properties can also be captured at a high level and broadly influence both high level and low level design. + +* **Low Level Design:** While high level design can be thought of as identifying key subsystems, abstractions, and relationships, the low level design process defines the concrete public interfaces, classes, and methods that will be needed to build the system. Design patterns are often applied during the low level design process to ensure the design will be amenable to future evolution. + +## Abstraction + +Abstraction is the fundamental technique used by software engineers to be able to manage the complexity of their systems. Abstraction enables engineers to focus on the _key_ information for a given task while eliding unnecessary detail. (e.g., a UX designer might focus on UI flows, but would not reason about backend cryptographic protocols). The 'right' abstraction will vary from task to task. The most high-level and common kinds of abstractions relate to control and data abstraction. + +### Data abstraction + +Data abstraction is the process of explicitly separating the abstract properties of a data type and its concrete implementation. The primary benefit of data abstraction is that it enables client code to be oblivious of the underlying implementation of a data type allowing it to be upgraded and improved without impacting client code. + +For example, a ```Vector``` in Java is implemented using an array, but the JDK authors could choose to change this implementation (say to a linked list) without impacting client programs. A Java ```HashMap``` is stored as an array of linked lists. In both cases, client code can be oblivious of the underlying implementation as long as they understand the behaviour of the methods provided by ```Vector``` and ```HashMap```. + +Here is a concrete example of data abstraction from [CPSC 210](https://sites.google.com/site/ubccpsc2102015s2/schedule/ubc-cpsc-210-robust-classes.pdf?attredirects=0&d=1): + +```typescript +// Creates a new team. +// Requires: A non-empty and unused name. +// Modifies: Team database. +// Effects: Returns whether a team was created. +
public createTeam(name: string):boolean {...}
 +``` + +By explicitly describing the data the method requires, modifies, and its side effects, client code can be completely oblivious of the underlying implementation used by ```createTeam(..)```. What we are really trying to do in this example is define the method's contract, that is its preconditions (expects), postconditions (provides), and invariants (must always be true). Correctly and adequately documenting these abstractions is important because state-based errors are a common source of difficult-to-diagnose faults in modern systems that type systems provide little defence against. + +### Control abstraction + +One of the major benefits of modern programming languages is that they provide an abstraction layer between the developer expressing themselves and how a machine will interpret and execute their instructions. Abstracting away how the code will execute is called *control abstraction* and was one of the early advances in software engineering productivity (as described by Fred Books in [No Silver Bullet](http://worrydream.com/refs/Brooks-NoSilverBullet.pdf)). An easy way to think about control abstraction is that it allows an engineer to consider *what* they are doing without becoming overwhelmed with *how* they are going to do it. + +It is easy to take control abstraction for granted, but the difference between the following Java and Assembler snippets demonstrates the benefit of this abstraction layer (example from [Beyond Java](http://www.beyondjava.net/blog/java-programmers-guide-assembler-language/)): + +Java: + +```java +a = b + 1; +b = c + 2; +c = a + 3; +``` + +Assembly: + +```assembly_x86 +mov r11d,2h +mov r10,7d56d18b0h +add r11d,dword ptr [r10+78h] +mov r9d,dword ptr [r10+74h] +mov dword ptr [r10+74h],r11d +mov r11d,r9d +inc r11d +mov dword ptr [r10+70h],r11d +add r9d,4h +mov dword ptr [r10+78h],r9d +``` + +Dijkstra coined the term 'structured programming' to describe language constructs that are designed to allow developers to express their programs using logical blocks of code that execute in sequence with control statements for choosing which blocks execute and subroutines so programs can be more meaningfully decomposed. + +Language constructs are continually evolving to enable developers to better focus on their task intent instead of worrying about underlying execution context. JavaScript promises are one such example as it allows sequences of asynchronous operations to be developed without resorting to nested callbacks and written in a way that mimics synchronous method calls. + +```typescript +// Manages team creation flow +// Defers to other methods for operations +makeTeam(teamName: string, memberName: string) { + var that = this; + // create team +
 that.createTeam(teamName).then(function (teamId) { // createTeam is async + // add member + return that.addMember(teamId, memberName); // addMember is async +}).then(function (success) { + // ... +}).catch(function (err) { + // ... +}); +} +``` + +## Decomposition + +Decomposition is the process of taking a complex high-level entity and splitting it into more manageable smaller pieces. One of the main goals during decomposition is to make simple tasks simple while ensuring that exceptional tasks are still possible. There are many kinds of decomposition strategies (e.g., based around program units, algorithms, subsystems, etc.) but the most general (and common) is top-down decomposition. + +In top-down composition a description is broken up into pieces starting at the top and working to greater level of detail. Initially this will mean that many important details will be represented by 'black boxes' (which can represent systems, modules, or any other relevant level of abstraction) that can be filled in in the future through either further decomposition of the box or by providing final details of what the piece does. Working top-down is a great way to have global awareness about the full system but can be challenging once terminal boxes develop constraints that necessitate revisiting prior decisions. + +An alternative (also common) approach is working bottom-up. In this way decisions can be made about leaf nodes that can be composed into the final overall system. This approach is great when you have concrete details about the team implementing the system (e.g., because you can have them make the most relevant decisions for their team right away). A downside of bottom-up approaches is they often lack the global overview which can lead to inconsistencies and extraneous focusing on details early in the design stage. + + + + + +## Information hiding + +Software engineers often talk about abstraction in terms of _information hiding_. Information hiding was first proposed by David Parnas in 1972 as a means for separating the parts of the program that are most likely to change from those parts that are more static. Information hiding is a high-level motivation for APIs: by describing the expected behaviour of the API you can "hide" the implementation behind it. + +Information hiding is a specific, common, and important form of abstraction that intentionally seeks to identify 'that which varies' from 'that which stays the same'. This is important, because all abstractions in code come with a cost: trying to understand a system with unnecessary abstractions can add complexity and difficulty, while balancing this complexity against trying to evolve a system lacking necessary abstractions is a challenging task. + + +## Encapsulation + +Encapsulation is related to information hiding and is practiced most concretely in object-oriented programming languages like Java, C#/C++, TypeScript, etc.. Encapsulation is concerned with delineating the contractual interface with its implementation. The most common language feature for supporting encapsulation is the interface whereby the interface describes the public contract an object will provide and the concrete class describes the implementation of the interface (along with any supporting private methods and fields). + +## Constant change + +As Jeff Dean noted in his [WSDM 2009 Keynote](http://static.googleusercontent.com/media/research.google.com/en//people/jeff/WSDM09-keynote.pdf) it is important to recognize the parameters that lead to the abstractions you choose will change over time, and often by many orders of magnitude. The right design for one system will probably be different at 10X load or 100X load. While it can be tempting to try to design for the 'end game' there are high real costs associated with premature optimization. In his talk, Jeff advocates designing for 10x load with the expectation that a rewrite would be required at 100x load. While one could view this process as wasteful, an alternative viewpoint is that in the time between 10x and 100x you will learn things about your system you would not have known in advance (and would thus not solved your 100x problem from the start anyways). + +Thinking about change is also related to encapsulation in that thinking concretely about what parts of the system are likely to change in the short and medium terms are more likely to lead to useful and valuable abstraction layers than taking an 'anything can change' view to design. + +## References + +* Original [information hiding](http://www.cs.umd.edu/class/spring2003/cmsc838p/Design/criteria.pdf) paper. + diff --git a/reader/content/high-level-design/apis/API_star.png b/reader/content/high-level-design/apis/API_star.png new file mode 100644 index 0000000000000000000000000000000000000000..ef98a56aae283361a2827828f2f52ce900006422 GIT binary patch literal 105243 zcmeEuWn5HW7cL;6f(VL~q#z<)(lB5E(h|eaDIFu-AS#NaN;iUZ3`jRBAuZh<149fo z#0A8Fv=5a8qB z;9tB%h`o~U_cRduAHLOnmHRk2rIExZrUcl(nam$+sNmpu-@(Co{t5>NiM{lE6$i(S z4+jTkf`cO-kAp+?EV)))5(fuY*G5anRYyfx+#Kw{WBL?qX2Ij>@C^G692`kcaqO=S z7OtjDo(}eoF5;e2%s)RNj{W`YGA}dJ&yTp;NiplFJYjkWcD7&=;mldQd1T>Y?rihS)duXyboN|RGq9Vh6f^T#Lx2AL>ZhxX<$qdobou>S*caqI zyTi-R!^ivQ+1Rg2o?R7JbGEU-wtV({0KeqVPyX%N?{*}4&l>+rXMVlu&#Ty11rSN{ z{uwp^k@8nIT^t-49EC^swLEb*rip!T3{P+G2-YR_NZoeU{P;oV2~4w9akJn>`Kmeb zEn+iDKAD)%7X;woy9?YQOqX7meGK{DN_6+pwd*{9846oQeJ)9@E?HJ4CJ}{83c}+X zTlIp?Py2Y1p0YiCNa>F{wudGQLWO59v!03x%3lIpVtS5)_rD+iUyQ*$Dcr0zmA5pv zeocwYJ>sSO{H$*uSbJ0rzNzzD|H#d-Ze&MK9UVlP_+)OVG7xbff3i0(SVBn%04vPR$-Kmu89-7#T|d5m`w6Jmxmb z$>eurs4u*NJO=kxc8(wvtfNqAH6mPmG8r7~k6gp&&rLP~$+BmEV6Q~sNo{#r8<_Qb z^wIP%Y}|};+Kd+=4wsb2zbPvNbP$n90gf!b%wq;W24*aL=N&EkBAuBdk=7!r|Ao~d zNyX0|HigI;1Ua5IzQWW;p~`cXa!M^xHk1&qlnMC4dv27ZN4LE`jOEt=G}o}1h#m4I zaLWB^J}{U_hugv#L*e|ooE;aa?_lGrBJ}C=DnIh=7q%CH4{ncl0R&$Jw%fe$ms5)? zDEu0QfPe##k`i^1wPq{t_L2M0`o)P4- z`n7#dtUkgRIn>)Nt*r8F8(D4*d3F98ee8f6HWOa>vpr=7$)8-kL5T0FV?%Q)1(WbX zu*Qb(sR`eus#4aeYp5JapeHYGJW3c9sTnC2qNEtnMjzEcDj95dHY-*9LP+7M^i$Sd zM(g?zsR@+NVoH#Zd!<~sn`jEffhLD%YnDSIN&No8JC7S#bM|i1-M&47c#&UYJ&* zXdTLL@|NIY9GQOtT|wMd!%UT9co4Vt4%lx3Q_{X~qMsuS+* z>a)3)&w&>(s|}Rhm_CV>Omnx1Q43Ggea9O&EYjfJZ!8ykQ79Er<0+aO%=LTeB`@Ly zHl-&oQX2ryuSntLQ+oC{QlclRL9CIZWae8c4x6#ilx&8=4|_RL!Un1g;RJ3ugBB?c zv=eMY&m-Pgd4x-Oea(obGCq_EJWXwqL+4f=^_=25nC*2J5}$b8`pgiYN^z9?tR|ix z!UQUIskO;6Um130L`AZHTbBIKi>k;T(fpaq2XhP#?IR&>izqs&a;x%o!~UK%S8ZdE zyZgb8YgkXMn2^}kbl|94^0dR)tB1oBwu;vGTc@njHh*vdW;UJa%hlqy?VBAdNQ!mSSbq1_&*Y<=6GK()&h< zn}NYNDj>-{$H1arq~~I9{7xJz1;xedYSELeETGeBr4HY`EyJf3>(u=I8gZFEmC^=4 z^;p))RX>D_B^6tX?x=fBp~F`2WkI}R_0(_dYjXQy78_&)sVVCiTl~plrK){>h78qD zp+m3ANgur}h~ig$|KW!DCqyRv4X<(Bo=AC>q48)on=|^^fZZJuj8*Kxo)x~lU%nJ@ z(n&Oy{>``k%&WiV4EkCo^CWb;=6(n8_vU!c^TT}UNd4^_k8a_6TU$4y`u)0-g5(;% zMCv`s$CT9Ncqtu33JrQ>OdpPCu%2y`Hbch9Qf8u zTrEu^B-J%7y4yqK(cLvMc2POG!c|uVf%zeAWu@`MXW2I2F52o-O==e z1;nt_g*V?eXwRek zL^@|LeRwH;cEhd7%_qWqKO^h7iDY;vUnlZ42c)4Jq`m0H{RBqMw~flbATeN9NWS|D zc(OS8xerdap@W3c9$$E>RT^dcfr3 zk3kRj$3|Va-KtAe>*45OV>C0(@8#vCy-B-=x)pibXrKm_HF8v*6}VRtdh3FZAGMCQ zW-sTEuh7RH*v`deRzoLYw>`*2Rm17~TTN<`VyUu>5MbM~h!=%Y>6Cunhy zd6-INqT&G^FCapAFLIf}4^o)hC=YC{%ZJ`AXZy|~r6zUB+86D@9CSVkmp(rKgJkc0 zl5vPs7G4sKP>&=nWjwMBY*MT8EC+cP6ufTGTCc+eC+(Of4Uo7Td~d>^o~}^#DfBY@ z*aRWZE2K%9XmO0MhCWiI5O(->DKcka2{kWi_H2|`02$$R9PREbEq1ta-~pe$UQ)0T z#J=3uRBp2Q(z#*Ib$c-@(u3XBXZZqSP=$hA_Q8>p?-Rc5#IUiXHWlOD``e<Rvc4YKA&6?nvdG~#3%kYGi;A!+B&|7?FW#Xhw->!IQP{@xN zA$arXQU$;}=-}}-j9l7Q@13@fS7x6lB#rF7k3n_zdN$qp+Tg?2xn=$M*`wc(Dfjx5 z3oiomztet5(*f~4X=)y2tagI1SyPRiKpY;z8a-u?UItD`z+OYb3OK2A4{Kbtw*=nz zBkt5-{8%rWhf*CXMs>Xy99g)48_F0Ot&N+ci9af``_oC$U8#TQ6KQVvMWzA0>m``; zHq?K3a#cy1fh(MLXQx|#aQX9(wKl|iC=+;S2X#;>vE&k-P-!2($Lm$EtuRwFe2mAf zt(($+A&$XD#B2>;;oO&{U&Ft2Nf=My;qTx?<{Qx}?c*bm-sS#M*(8bFR$g|NyQ_83N9wN^&3MZ4NEek?5~)nriJ1s3 zkZk&m9^Na>>zWi~?1CtFc|J~UpW+Px#O_t((2mL*$526#8fx8n8dJhH!_szX#`wLq zN!|NBHE2zYq-q#HG}e5$K*q-xrNDXI5M>06Vc?EvPF}r!KK{9~SjfyRn*7Vs-~?u~ zo1lu*izLf1TRIhTy>FrpY$Gu@5#>cMzs>wu{CZ<_FYl!`6^+9;Da!7o_xkKWz(Pn* zzwkYqAG59A;XdRt>QoQFZWAcOgs={ofW!EsHuxi|-a=kje zFu2sN_lQO*j13+|FB+MG+GiI(iu{m*0>^BBfwu0BkM$gvFNXTAD9(b^U%W4Z)Olr zspC)Y!&&AqhP6&@<~Pol*IYS)wq4|0{zUI*IeDaNzMS|}=7Lko6y5D9O78~e_u|$? zYr}zUmqxZCd&uEeAT#BQZ&*5eCTd<^zGTb5uWE%5(v99-Abwp`coLMl5I#!v~rt}T3L0T})ObB{Z!y$R66^9!L24Cn0 ze9drGEyuWQ{Q8SO?5PSaW>Lm)#+qds=IPgrJWku%d7WTvV1f_dZ{%tEp}kI2A>OWe zBaLnOD>U32Fr1n%wZKJcnw$?1u8c%%N5BsG4w@vFdtq;r17A(UI8Xd?RzO@j$;MWE zPts;eIqXn^d@8Sh+Z9~=2;w266206x;*_~ZWLdE#R20E%swkmW$ z8?-?U&3k}r;HrQC)6h@67hOBX25#-mbY@i)BKGgf^6#SPLk$~eFCNHWJ>Ra@lDK#; z!mWB9|9%m&TY*z2ewU^F@1fcjUP_1M=HKmd7Ow#92@R_$pNQzmY#(Jd@I+QbHI@cv zT$)`|9o4Tt1UWMzyxq08WR%(~k6WP&+au`%m#n_+=bd;@8;2V|<+XF5MIP8F+i7Q` zJ`lg=&OKZljtt^Is6_=-)%78zm6W*DP_d(8utXudeDik+2?@KY!fusWc;{$)8c$Nd z*L~Kw_*cIJ%k<`oX^tHnx6;a7y1x2!UWL!pc4iV>h!Ka3aSmt<`4v_barJy#d~Vsxr^yFO}IAqsFIYuqWryDl%v{q*FI~gs22KP`~;Fr ziD??@mVCMHC^f;wUtRcRZ-QmaF;BB`@5sU>Ul`_a2ViRMMUy%2o(eM6V2+y{N$jBr;;8LK7pAbW4 zh)8tHueQkzzC!SY>W5_O=}85s9JTijl{TFP7QOCkbjABq?PG*j642_aa&$DPg1F2! 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b/reader/content/high-level-design/apis/_index.md new file mode 100644 index 0000000..ae7c3f6 --- /dev/null +++ b/reader/content/high-level-design/apis/_index.md @@ -0,0 +1,56 @@ +--- +title: "APIs" +weight: 2 +--- + +Application Programming Interfaces (API) describe the programmatic interfaces used for interacting with a software system. APIs can exist on many levels, from methods to classes to online services. APIs may be used by user interface code, by backend services communicating with each other, or by third parties. The primary purposes of APIs are to provide a predictable means of programmatic interaction and to hide implementation details from the API client. By hiding the internal implementation, APIs enable the API owner to change their underlying implementation without affecting any clients that may depend upon it. + +{{< figure src="API_star.png" >}} + +While APIs have been defined and used for decades, the advent of web-based services has propelled API design into the the forefront of many organizations (typically through REST-based interfaces). This is because services can be hosted behind APIs giving API owners an easy mechanism for maintaining and updating their systems and controlling billing without having to release the actual source code for their systems. + +One consequence of providing a public API though is that once clients start to use them they are difficult (or impossible) to change, leading to the saying "API are forever". Because of their long lifetimes, it is important that software engineers carefully design their interfaces to make them easy to understand, use, maintain, secure, and extend. + +API design is an iterative process and good design requires collecting a variety of viewpoints (both internal to your organization and externally from engineers who will have to understand and use your API). There are typically many different design options for any given API, choosing the 'right' one will require balancing some of the concerns mentioned in the last paragraph. + +{{< figure src="API_usability.png" >}} + +One aspect of APIs that is often overlooked is their _usability_. The user of an API is the developer who has to learn to use the API to use it within their own system. By providing effective affordances (such as meaningful names, parameter types, and data formats) and thinking about how the APIs will be used in practice, API designers can ease adoption of their APIs and help prevent consumers from using their APIs incorrectly. Usable API design requires combining strong development practice with techniques from user interface design (such as prototyping and user studies) and effective documentation. + +## API platforms + +API Platforms are now an extremely popular way of building business value. These platforms emerged in the early 2000s when companies realized they could focus their development efforts on building a core set of functionality that could be extended by customers or third party organizations. By focusing on maximizing the quality and core capabilities of their platform, companies relieved themselves from building all possible niche versions of their product, instead letting other developers extend the platform for them. + +API design is of critical importance for companies building API platforms as a set of APIs that is opaque and difficult to understand will not achieve wide adoption. This is especially important for organizations who intend their API to be their primary product. + +## API design principles + +At a high-level the principles behind good API design mirror that of good design in general. The first two questions to consider when designing the API are: + +1. What is the goal of the API? +1. Who is the API's intended consumer? + +These questions will help drive more technical answers like which languages, protocols, and data formats should be used, along with how the API will be versioned, licensed, and authenticated. + +If APIs had one design tenet it would be: + +> APIs should be easy to use and hard to misuse. + +APIs should not be designed in a vacuum. Starting with a one-page spec that describes the API goals and users is a great place to start. Next, as many stakeholders as possible should be looped into the design to ensure it makes sense and adequately addresses their needs. While automated testing plays an important role in API validation, it is also important to create several proof-of-concept client systems that use the APIs to ensure it can be effectively used. While all APIs will naturally have some fundamental shortcomings (an API cannot be all things to all users), by looking at the client code you can gain an appreciation for how code may be duplicated, awkward, or erroneous when used by API clients. + +Some high-level API design advice includes: + +* APIs should do one thing, and do it well (also known as the single responsibility principle). + +* APIs should be kept as small and simple as possible; once an API is released it is hard to remove. + +* APIs should _never_ expose internal implementation details; these make it difficult for the consumer to use and for the maintainer when they want to change an internal implementation detail. + +* The 'usability' of a system to another engineer is dictated by the quality of the API. This means names should be well considered and consistent and the APIs, their constraints, and their pre- and post-conditions should be documented. Consistent APIs reduce developer surprise and helps them to naturally move from one API to the next. + +## References + +* We will expand on design in general later in the course. Joshua Bloch, the designer of the architect of the ```java.util``` package has a great set of slides on how to design [good APIs](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/32713.pdf) online. + +* Many popular libraries have great APIs. While the [JDK](https://docs.oracle.com/javase/8/docs/api/) and [Android](https://developer.android.com/reference/packages.html) are huge, smaller libraries like [Guava](http://google.github.io/guava/releases/19.0/api/docs/) and [Joda-Time](http://www.joda.org/joda-time/quickstart.html) are also instructive. + diff --git a/reader/content/high-level-design/principles/_index.md b/reader/content/high-level-design/principles/_index.md new file mode 100644 index 0000000..b507e0f --- /dev/null +++ b/reader/content/high-level-design/principles/_index.md @@ -0,0 +1,125 @@ +--- +weight: 1 +title: "Design Principles" +--- + +Design principles are high level guidelines that can help ensure our designs are robust in the face of defect fixes and feature additions. There are a great many design principles, here we only address a small subset of those. One thing to keep in mind is that these principles are not absolute: sometimes you will violate them, but if you do, you should be careful to do so in a considered manner. + +## Designing for evolution + +Designing systems in a flexible manner is crucial, given that all successful systems evolve over time. Making intentional decisions about the coupling within our system and cohesion between program elements can help guide us towards ensuring that our designs are amenable to future evolution and defect fixing. + +### Coupling + +Coupling is a property that indicates the strength of connections between different program elements. Strong coupling is problematic because it negatively influences the evolvability and maintainability of a program. There are several reasons for this: + +* Coupled code makes it easier for errors in one part of the system to propagate to other unrelated parts of the system. + +* Coupling increases the degree to which a single bug fix or feature addition is scattered across the codebase. + +* Code that is tightly coupled is much harder to reuse independently than code which is loosely coupled. + +* It is harder to understand a source code element that is coupled to other elements because individual elements cannot be considered (and understood) in isolation. + +Two program elements are considered independent if they can function without the presence of the other. It is important to remember that any non-trivial system _requires_ that there be some coupling between elements. The goal is not to eliminate it but to make the coupling be as loose as possible. There are three primary ways to decrease the coupling between program elements: + +* **Minimize the number of interfaces between elements**: The more interfaces two program elements need to share, the more tightly they are coupled to each other. + +* **Minimize the complexity of interfaces**: Since some coupling is often needed, reducing the complexity of this coupling to its core elements can make the interaction between the program elements clearer and easier to reason about and evolve. + +* **Avoid control flow coupling**: It can often be convenient to pass objects that control the flow of computation within another element. While this is ok if the element being passed is some type of data structure, it can be more problematic if the control flow is being influenced by simple control flow flags (e.g., some kind of `boolean` flag that takes one program path over another). + +The flow chart below can be helpful for reasoning about the coupling between program elements. One thing to note is that not all coupling is equally detrimental: coupling elements by simple data types is less problematic than coupling them through global variables (common coupling) or internal field access (content coupling). + +{{< figure src="coupling_flow.png" alt="Coupling flowchart" >}} + + + + +### Cohesion + +Cohesion is a property that indicates how focused our program elements are on performing a single complete task. This is easiest thought of in terms of classes in object-oriented design. In this space, cohesion measures how well the elements within a class belong together. Classes with low cohesion are responsible for a wide variety of tasks; these classes are harder to reason about as they often have many competing concerns within their implementation that might conflict. This can cause maintenance problems because changes to fix one defect within a class might actually be by design for another feature provided by the class. The larger a class grows in scope, the more likely this kind of problem is to be be encountered. + +Cohesive classes generally have a small set of private fields that make sense to the majority of the public methods within the class; if there are fields within the class that are only used by a small fraction of the public methods it may be a sign that the functionality provided by those methods and the private field may not be cohesive with the overall functionality of the class. + +Since cohesive classes are smaller, they lead to a proliferation of classes within a system. While this might make it harder to find the right class within the system, it greatly eases how hard it is to understand that class and simplifies any future bug fixes or feature additions that may be required. + +The flow chart below can be used to reason about the kind of cohesion within a design. As with the coupling flow chart above, some kinds of cohesion are better than others. Thinking about the cohesiveness of our program elements can help us to understand when further decomposition of our designs might be helpful and will also motivate the organization of our program elements into their most appropriate subsystems. + + +{{< figure src="cohesion_flow.png" alt="Cohesion flowchart" >}} + + + + + + + + +## SOLID + +Design principles provide guidelines to help us reason about specific properties within our designs. It might be tempting to treat design guidelines as rules, but that is not their intent: designs will often contravene well-established guidelines; in fact, many guidelines themselves are often in tension with one another forcing engineers to think about their systems to determine which principles are more important to their system. + +There are many catalogues of design principles. While some of these are broadly applicable, others will be unique to specific domains. One of the most commonly-used catalogs are the SOLID design principles. + +### Single responsibility + +As systems grow it becomes harder to understand them, fix defects within them, and add new features to them. The single responsibility principle says: + +> A software module should do one thing and do it well. + +One reason this becomes problematic as systems grow is that it often seems easier to add code to an existing module than to create a new module from scratch. This means that code gets added in places where it might not fit well and this mismatch can make the module harder to evolve. + +Many design patterns have been explicitly crafted to encourage designs that adhere to the single responsibilty principle. For example: + +* Strategy pattern: In this pattern, modules encapsulate algorithms; this means we create modules that _only_ implement a specific algorithm. + +* Command pattern: This pattern separates the notion of an action that can be performed from its implementation. This results in small modules that only provide the features needed for a specific action. + +* State pattern: Systems often depend differently according to their internal state. Rather than having large modules that need to reason globally about all states, this pattern encapsulates the behaviours for a single state in a single module resulting in smaller, more targetted code. + +### Open/closed + +The open/closed principle states that modules should be: + +> Open to extension but closed to modification. + +This principle encourages software engineers to design their code that is more amenable to future change. Specifically, the open/closed principle encourages engineers to think explicitly about what parts of their systems should enable future feature additions and which parts of the system should not. This distinction is important, because extension points typically add abstraction to a system which makes them harder to understand. We might want to explicitly inhibit some kinds of extensions as well due to negative performance or security implications. By explicitly planning for future extension, new features can be added in a way that does not cause existing code to be modified. + +This design principle is explicitly supported by most design patterns as these predominantly describe explicit extension points (e.g., by adding new strategies, states, commands, decorators, observers, etc.) that can be extended while existing code remains oblivious to the new features. + +One of the biggest challenges with this principle is knowing _when_ to enable extension; this usually takes explicit discussions with system stakeholders to reason about the explicit costs and benefits of such an extension mechanism. + +One common code smell for violating the open/closed principle are `instanceof` or `typeof` checks within the code. These checks mean that if a system is extended with a new feature, client code will probably need to be updated as well so that the type checks also check for the new features. This is one of the primary reasons to be wary of this kind of runtime type checking. + + +### Liskov substitution + +The Liskov substitution principle says: + +> Any object can be interchanged with any other object that has the same parent type. + +For a complete coverage of this design principle. Since this has been covered in prior courses, we will not discuss it further here, but you are encouraged to watch [Elisa Baniassad's video](https://www.youtube.com/watch?v=j6jbTMpZkWQ) for a more complete description. + +### Interface segregation + +The interface segregation principle says: + +> Clients should not be forced to depend on interfaces they do not use. + +This principle exists because as we evolve our systems we often do so by adding new methods to our interfaces rather than by creating new ones to succinctly capture the added feature additions. This principle can be thought of as applying the single responsibility principle to interfaces (instead of to classes) as it really pushes us to design our interfaces to be as small and well-focused as possible. + +### Dependency inversion + +The dependency inversion principle says: + +> Classes should depend on abstractions, not implementations. + +This principle helps engineers to design implementations that are as decoupled from one another as possible. This usually happens by injecting an interface between the two concrete classes and having the classes take dependencies on the interface instead. While this might seem like a small difference, it means that if you wanted to reuse one of the classes you would also only have to reuse the interface rather than the concrete class and all of its concrete dependencies. + +When we refactor an existing system to encourage extensibility, we often do it through dependency inversion. Specifically, we introduce a new interface and make the existing code implement the interface. This makes the post-refactor code both easier to reuse and extend. + +## References + +* TBD + diff --git a/reader/content/high-level-design/principles/cohesion_flow.png b/reader/content/high-level-design/principles/cohesion_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..6deafaef3cdeb6fabb5dd91b10c1f2312fbc6d1f GIT binary patch literal 219164 zcmeFZX*iYb+dgcLB4de)mAQ-=l6eW4DJscWVUaQOY@tGwDP+!^lsPhMCNq^}&Rj$& zGymi2dG5N_{rrF1`+j>rywClit=7%8&g(pnVL$d`KhCf-I;u3gS$7i=5z(ltDeDmt zQDBLP$k-_<;1f+v?#Dz##D+FXN@vuSlrU$U9j$EaEQyHtqOY2p>!}OyHC?`JZr;?! ze{{FAhhAuCgr0drN8{_pw~Z~0&&?C>8yk-tpc^4Q_kt+v?DI-n>IlqwsZi77yqYzu z?0GktZ*?8qjzz9B)LK9J@ci6QPqL`(5Px|;9+b&ILGgwOV^md}P4wzJQK`yp{9TgL zDg37yx?dD@GE^_*NiHxlj8j_-6P50Z5n~9n64@pyb&H95)f4^cjE^F|pS$cChPOoT zNaKCHZ8H&f{yuKZXQ_q#?S<27p4`}4?PV;K^mfn4u;#Mv-^#hTRhueoJ_iCOl z5u+@b+@Pke*x1?GX*hUf@@abDrbDZ?J{v}q@ zm%QelKPW^*C>^hwk8J9vSlN-qDt)|U?!Cq zzHELnY8R2}4lWscSHa}>CZD_LdQn?T`l_S7&}9q9E0#i@_D*mJL`1Tl((qS%OV`U7 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zLC>lt1y1;fc>cy8J;DCdIVtmi@xQE!iu&)Ln>7=h@K0LJgMSBG;rYl`Y`d@hQU230 ze*amVDmdYzh&NvU4*Np;k>0FhO(@F0f9@|x0NT54K=R)K=&WU=llP2`;U9PB^0z;b z#BYQ73f*M^_3{gVh_nmtLdrP_!9VWKrElRskred*hRuH(%Kx7Yo0*F%w_`}SzIVNS RiUR(-E2na& +{{< figure src="uml-overview.png" alt="uml diagram overview" >}} + +Unfortunately, given the ambitious goals UML tries to achieve, it has become an extremely complex standard that is rarely used in its complete form in practice. At the same time, technical stakeholders often create visualizations of their intended systems, so being able to interpret, understand, and generate some key diagrams is often helpful in practice. In this reading we will provide an overview of a small subset of UML that is often practically useful without being overwhelming. + +## Class diagrams + +Class diagrams are the most common form of technical abstraction for software systems. Class diagrams are static representations of the software elements and their relationships within a system. For object-oriented systems class diagrams detail the key classes, fields, and methods within a system and how they relate to each other statically. While class diagrams can contain all program elements, they typically focus on a key subset of elements (especially with respect to fields and methods); this does not mean that only public API should be shown, but also API that are intentionally not private. These diagrams are an excellent abstraction for developers to reason about and discuss various design alternatives. Class diagrams can be translated fairly directly into code skeletons (and vice versa). + +Classes and interfaces are represented by simple rectangles that detail their names, fields, and methods. Method and field visibility can be clarified by prepending names with + (public), # (protected), and - (private). Three primary classes of edges exist in class diagrams. Large hollow arrows represent inheritance relationships. Dotted lines with simple arrowheads represent dependencies (typically through method calls and field references). Solid lines with filled diamonds represent composition relationships; these relationships indicate that the class on the diamond-end creates and is primarily responsible for the lifecycle of one or more variables of the linked type. + + + +```typescript +interface IInsightFacade { + addDataset(id: string, content: string); + removeDataset(id: string); + performQuery(query: QueryRequest); +} + +class InsightFacade implements IInsightFacade { + // impl here +} + +class QueryController { + performQuery(datasets: Dataset[], query: QueryRequest) { } +} + +class DatasetController { + addDataset(id: string, content: string) { } + removeDataset(id: string) { } + getDatasets() { } +} + +class Log { + trace(msg: string) { } + info(msg: string) { } + warn(msg: string) { } + error(msg: string, stack?:Stack) { }
 +} + +class InsightFacadeSpec { + it(‘…’) { } +} + +class QueryControllerSpec { + it(‘…’) { } +} +``` + +Translating from class diagrams to/from code is fairly straightforward. A more common use case is to translate from a textual description to a potential class diagram. For example the class diagram and high-level descriptions below are different representations of the same task. + + + +## Deployment diagrams + +Deployment diagrams overlay static class diagrams onto the execution containers and physical machines upon which they will reside. Identifying how and where a program will run is particularly important for mobile, distributed, and cloud-based applications. Key stakeholders typically include developers, operations, dev/ops, QA, and infrastructure teams. + +Simple deployment diagrams might only concentrate on mapping modules (or classes) onto machines, but in more sophisticated environments could include mapping the software into specific execution containers (e.g., virtual machines, container images, etc.). + +promise states + +{{< figure src="uml-deploy.png" alt="promise-states" >}} + +## Sequence diagrams + +Class diagrams often hide key details that are crucial to understand before key aspects of a system can be developed. Sequence diagrams focus on how code elements communicate with each other capturing the order of execution of particular methods and the data being passed between them. These are typically used by developers but are often also used to capture complex interactions which may also involve dev/ops, security, or usability stakeholders as well. Sequence diagrams specifically aim to model protocols and dataflow through the system. Given the complexity of these diagrams they are usually only generated for a limited subset of important, constrained, tasks. + + + +## State machine diagrams + +State machines are another representation for capturing dynamic interactions. They are extremely helpful for modelling event-based systems, which accounts for most user interfaces. One of the hidden powers of state machines is they enable state complexity to be actively reasoned about and reduced by explicitly considering the 'key' interactions rather than trying to support all possible interactions. + +One use of state machines can be to identify all possible actions and to name them effectively: + +{{< figure src="uml-state-water.png" alt="state diagram" >}} + +A more common example is to detail how the state machine is entered/exited and to show both the possible transitions and capture cases where transitions are explicitly forbidden (e.g., within settled states): + + +{{< figure src="uml-state-promises.png" alt="state diagram" >}} + +## References + +* [JetUML](http://cs.mcgill.ca/~martin/jetuml/) is an extremely lightweight diagramming package. + +* [ArgoUML](http://argouml.tigris.org/) is one of the most comprehensive UML diagramming solutions available. + +* [Example](http://www.uml-diagrams.org/examples/bank-account-domain-diagram-example.html?context=cls-examples) of a simple bank account modelling task. 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reader/content/introduction/async/index.md rename to reader/content/introduction/async/_index.md diff --git a/reader/content/introduction/languages/index.md b/reader/content/introduction/languages/_index.md similarity index 98% rename from reader/content/introduction/languages/index.md rename to reader/content/introduction/languages/_index.md index a9e727c..6f36440 100644 --- a/reader/content/introduction/languages/index.md +++ b/reader/content/introduction/languages/_index.md @@ -231,6 +231,3 @@ TODO: This should be added next year Article about some rationalization about moving away from OO: https://medium.com/@cscalfani/goodbye-object-oriented-programming-a59cda4c0e53#.xtqu1scch ---> - ---- -[![](figures/CCSA.png "Creative Commons: Attribution-ShareAlike")](https://creativecommons.org/licenses/by-sa/3.0/) [Reid Holmes](https://www.cs.ubc.ca/~rtholmes/) diff --git a/reader/content/low-level-design/_index.md b/reader/content/low-level-design/_index.md new file mode 100644 index 0000000..44fe4fe --- /dev/null +++ b/reader/content/low-level-design/_index.md @@ -0,0 +1,21 @@ +--- +archetype: "chapter" +title: "Low-Level Design" +weight: 5 +--- + +While high level design principles (like an emphasis on low coupling and high cohesion) provide broad guidance while considering design decisions, lower level design is about deriving concrete designs that can be directly implemented in programming languages. To accomplish this, low level design relies on more specific design guidance, although this guidance can be easily associated with the [high level design](readings/DesignPrinciples.md) principles that they codify. Three commonly used low level design principles are: + +* _Encapsulate what varies_: A central low level design goal is to identify parts of the system prone to future modification, likely to need future extension, or could be reused in other contexts and encapsulate them to ease these future tasks. +* _Design to interfaces_: Coupling to concrete elements is the primary inhibitor to the above encapsulation; by designing our systems around interfaces, evolving or replacing concrete implementations becomes much more tractable. +* _Favour composition over inheritance_: While it is tempting in object-oriented languages to reuse functionality through inheritance, this means types must extend other concrete classes. While inheritance is a powerful tool and is often the best mechanism for a problem, composition and delegation is frequently a better choice for supporting long-term evolvability. + +One of the most commonly used mechanism for creating low level designs are design patterns. While catalogs of software design patterns are relatively young (e.g., the Gang of Four published their [seminal work](http://tinyurl.com/yxzjjmng) on this subject in 1999), the power of using patterns for understanding diverse sets of data have been widely understood. For example, in Christopher Alexander's et al.'s seminal book [_A pattern language_](https://en.wikipedia.org/wiki/A_Pattern_Language) on architectural patterns, the authors note that: + +> “At this final stage, the patterns are no longer important: the patterns have taught you to be receptive to what is real.” + +This is notable because it emphasizes that the important part of the pattern is not the pattern itself, but the commonly occurring problem the pattern solves or the commonly desired benefit the pattern imparts through its use. By focusing on these problems/benefits we are able to understand the key design problem we are trying to solve, rather than being distracted by the mechanism used to solve them. + +Blindly forcing design patterns into inappropriate contexts is often worse than the code without the pattern applied. This is because most patterns work through additional abstraction layers which can make the system harder to understand and maintain. + + diff --git a/reader/content/low-level-design/design-patterns/_index.md b/reader/content/low-level-design/design-patterns/_index.md new file mode 100644 index 0000000..51c9c48 --- /dev/null +++ b/reader/content/low-level-design/design-patterns/_index.md @@ -0,0 +1,359 @@ +--- +title: "Design Patterns" +weight: 1 +--- + +Several design patterns are designed below, these descriptions are not exhaustive. + +## Observer: Simplifying state update notifications. + +When a user makes a change to a program, they expect the change to be consistently reflected across the entire system. For example, if you delete an image file from a photo viewer, one would expect the thumbnail to disappear, the photo to be removed from the gallery view, and the total number of files in the status bar to be updated to reflect the deletion. A poor initial design for this problem can be seen in the figure below. While for this one action this design might not seem like a big deal, for a real application with hundreds of actions and dozens of views, the design could require dozens of classes to be modified each time a new view was added. + + +{{< figure src="dp_observer1.png" alt="simple delete example" >}} + +Ultimately a designer would like to decouple the state changing actions in the system from how they are reflected by the other components. The observer pattern enables this decoupling by leveraging dependency inversion to implement a mechanism commonly known as [inversion of control](https://martinfowler.com/articles/injection.html). While a traditional program calls the components it uses (e.g., your program calls methods in a library), inversion of control provides a means for a library to call _into_ your code. + +For our image deletion example, `Image::delete()` calls `Subject::notify()` which iterates through its `observers` list and calls `update(this)` on all of them so they know that an object they are interested in has changed state. Each `Observer` can then react accordingly. This design means that the `Observable` classes are not coupled to any concrete `Observer` objects, making it so new `Observer` classes can be added to the system at any time. The system is also dynamically efficient because `Observer` objects can be dynamically added (and removed) from observable objects as needed by the system. + +In this system, many different model elements would likely be `Observable`: PDF files might render differently in the gallery view when they are selected (to allow access to different pages of the document) or folders might give some hint about their contents in their thumbnail representation. The value of the observer pattern becomes clearer as the number of observable and observing classes grows. + +observer-based delete + +{{< figure src="dp_observer2.png" alt="observer-based delete" >}} + +It is important to note that there is still _some_ coupling in this design: observable objects must know about the `Observer` interface so they know how to notify their observers, and all observers must know about the objects they are observing. But one crucial aspect of the coupling is removed: the `Observable` objects do not know anything about the concrete subtypes of `Observer`. This means new observers can be dynamically added to an object at runtime (for instance if a new view was opened in a user interface), or the system could be extended by adding a new subtype of `Observer` without changing any of the model elements that the new observer might want to watch for state changes. + + + + + + + + + + + +## Strategy: Encapsulating algorithms. + +The Strategy design pattern enables encapsulation of algorithms. This lets client programs depend on the algorithmic interface without having to depend (or know about) the concrete underlying implementation being used. This allows new algorithms to be easily defined and added to a system without changing any client code. + +The strategy pattern is often used to avoid subclassing the client. In our example below, you could imagine `Client` being extended by `CelsiusStrategy`, `KelvinStrategy`, and `FahrenheitStrategy`. While this would work, it would mean that `Client` would have to be changed to add a new form of temperature conversion. The pattern also supplants the even simpler approach whereby the code would have a series of conditional statements to choose the right temperature multiplier (which would also require `Client` changes to extend): + +```typescript +if (tempScheme === 'C') { + ... +} else if (tempScheme === 'F') { + ... +} else if (tempScheme === 'K') { + ... +} else { + ... +} +``` + +In general, strategies are fairly constant at runtime (e.g., the concrete type of the underlying strategy will not frequently (or ever) change once it has been set). One challenge with the strategy pattern is that the client needs to know about the available strategies to be able to instantiate the one they are to use, although factories or dependency injection can play a role here to help insulate the client from this instantiation step. + + + +{{< figure src="patterns_strategy-example.png" alt="strategy diagram" >}} + +## State: Dynamically changing behaviour based on internal state. + +The state design pattern provides a composition-based approach for clients to manage their behaviour dynamically as their internal state changes. The current state of the system is dictated by a reference to a state object; the reference is dynamically updated as conditions change. Rather than having one large `if` or `switch` statement controlling state transitions, transition decisions are left to the state objects which only need to reason about their valid transitions, not all global transitions. + + + + +In the diagram below, `TCPState` objects use their reference to `TCPConnection` to call `setState(TCPState)` as the state of the system changes. In this way the client (`TCPConnection`) always knows its current state without being responsible for making sure it is correct. As the client performs actions on its `state` object, that object can itself update the client's `state` in response to any action. In this way the client delegates the responsibility for managing state transitions to the state hierarchy. + +The state pattern isolates state decisions which makes reasoning about how or why these transitions took place much easier (for example because one could add logging to `setState(..)` in a way that would be opaque if the state was determined by examining values in fields within the system). This typically simplifies state management as well as from any given state there is a subset of valid other states that the program could transition to; this means the transition code is much simpler than a global block which must consider all possible transitions. + +{{< figure src="patterns_state-example.png" alt="state diagram" >}} + +For example, the client could avoid change-prone brittle control flow like the following (this is a subset of what would be required in the example): + +```typescript +if (last == null || last == '') { + handleClosed(); +} else if (last == 'listen' && isOpen()) { + handleOpen() +} else if (last == 'established' && isClosed()) { + handleClosed(); +} else if (last == 'listen' && isClosed() { + close(); +} +``` + +Clearly the State and Strategy patterns look structurally identical. And, except for the `setState` method and the fact that every state object has a reference to its context (so it can call `setState`) they are identical. The difference lies more in the _intent_ of the pattern. Strategies are fixed at the start of execution, whereas the States change repeatedly and often during runtime. This distinction further reenforces that the most important aspect of patterns is not their structure and form, but what they _do_ and how they promote encapsulation and evolution within the system. + + + + + +## Facade: Making common tasks easy. + +The Facade is a structural pattern to provide a unified set of interfaces for a subsystem. Subsystems can contain a large amount of code that even if well designed can be difficult for a client to learn to correctly use. Facades provide coherent simplifications of modules for performing common tasks. It is not uncommon for a subsystem to have multiple facades for different client use cases. Facades are usually easy to implement once you have a complex subsystem that you want to provide a more unified high-level interface to. + +One important note is that while a facade can simplify a subsystem, it does not prohibit clients from accessing features within the subsystem directly. Facades are mainly a pattern of convenience to make it easier for clients without restricting their options; however, if a client does only use the facade to access the subsystem they are also more insulated from structural changes within the subsystem as only the facade itself should have to be updated to support these, rather than the client themselves. One way to think about facades is that they essentially insert a layer into the design between the client and the subsystem. In architectural terms this is a 'non-strict' layer, since the client can bypass the facade to access the internals. + + + +Consider the following `WebmailClient`. This class is tightly bound to all of the subsystem code; if it wants to compose an email with an attachment or an appointment it needs to collaborate with many different classes. The author of `WebmailClient` is almost certainly a different developer than the creator of all of those classes so they need to learn a large set of APIs (both which APIs to all, and in what order) to complete their task. Additionally, any changes to those APIs could impact their code; since there are so many direct dependencies the chances of a change impacting their system is not small. + +{{< figure src="patterns_facade-example_before.png" alt="facade diagram" >}} + +To ameliorate this, they talk to the developers responsible for the PIM code and ask them to create a Facade that is easier for them to use for these common tasks. The PIM owner creates `PIMFacade` that hides the internal details of the PIM subsystem and allows `WebmailClient` to have only a single dependency. This decreases coupling between the client and the PIM classes, and adds a layer of abstraction so the PIM subsystem owner can simply update the `PIMFacade` if any of their internal classes change in a way that could propagate to the client. This both simplifies modification tasks for the owner of `WebmailClient` as they are insulated from these changes, but also for the owner of `PIMFacade` because they know they can make larger changes as long as they do not need to change the facade API. + +{{< figure src="patterns_facade-example_after.png" alt="facade diagram" >}} + +## Adapter: Simplifying interactions with incompatible types. + +The Adapter pattern is widely used, especially in the context of legacy systems that cannot be easily modified, to enable objects to more easily interact with each other. Adapters often act as translators, enabling the objects and implementations used in one design to be converted to a format that is more amenable to another design. While adapters are often relatively straightforward, if the differences between the two designs is large, they can become more complex. + +In the most common cases, Adapter objects simply act as a wrapper for another object. Concretely: the adapter contains a field of the wrapped type, and exposes a set of methods that make sense for a given design. Any requests to these methods are then _adapted_ to the interface required of the adapted object. This often involves transforming both the parameters to invoke the wrapped object as well as transforming any returned values to match the exposed interface. + +The main benefit of the Adapter pattern is that it allows clients of the adapters to remain oblivious of the design of the wrapped object, while still taking advantage of its functionality. Although client objects could do these translation steps themselves, they would then be coupled to the wrapped object and would have to take on the responsibilities necessary to perform the translation themselves. + +{{< figure src="patterns_adapter-example.png" alt="adapter diagram" >}} + +In the example above, the `Client` needs to use functionality from the `MP3Player` and `AACMedia` frameworks, neither of which they are able to directly modify. But they would like them to have a consistent interface, despite the fact that they both have different requirements for actually performing the action (executing `play(fName: string)` that the `Client` actually wants). The adapter objects each only know how to deal with their adapted type to provide the desired functionality. While in this example both Adapters implement `FormatAdapter`, this is not strictly required by the pattern. + +## Factory: Creating objects. + +The design advice _depend on abstractions, not implementations_ is widely used, but it is impossible to instantiate an abstraction. An object must be created before it can be used, and when an object is created we must reference (and be coupled to) the exact concrete implementation that we want to have a reference to. Creational design patterns provide a means for enabling the creation of objects to be encapsulated within a specific object. While this object will have to know about the concrete types they are creating, they allow their callers to depend on their abstractions (assuming the instantiated objects have a more meaningful supertype). Providing a means for client programs to remain oblivious of the concrete types they are using is crucial to enable the open/closed principle to be applied fully within a design. + +{{< figure src="dp_factory1.png" alt="factory-less diagram" >}} + +In the class diagram above, we can see the shortcomings of the factory-less design as `Bank` is coupled to all three subtypes of `Account` so that it can instantiate the kind of object it needs, despite maintaining a reference to `Account` itself. + +{{< figure src="dp_factory2.png" alt="factory diagram" >}} + +The above design has been improved by having the `Bank` depend on a `BankFactory` instance. In this way the `Bank` remains oblivious of the concrete implementation of the `Account` they are using. This design does have some drawbacks though: every time a new `Account` is added the `AccountFactory`, which all clients depend upon, will need to be modified. + +{{< figure src="dp_factory3.png" alt="abstract factory diagram" >}} + +This final design is called an _Abstract Factory_. In this design the client code depends on a factory that itself implements an `AccountFactory` interface. This means that the client can be specialized with the kind of factory that is relevant to them. It also means that as new types of `Account` are added, only the factories that the new `Account` is relevant for need to be modified. + +## Singleton: Ensuring only one of an object exists. + +It is not an uncommon design constraint to only want to have one instance of some kinds of objects. The Singleton design pattern exists to fulfill this role. The Singleton is also a creational design pattern as it is involved with mediating the creation of an object. While the Singleton is relatively widely used, it is frequently misused and its shortcomings should be understood before choosing to adopt this pattern. + +Singletons are the simplest pattern in practice (although more care is required when implementing the pattern in languages that allow true multi-threading): + +```typescript +class Database { + private static instance: Database | null = null; + private constructor() { } + public static getInstance(): Database { + if (Database.instance === null) { + Database.instance = new Database(); + } + return Database.instance; + } + // rest of Database +} +``` + +The `private constructor()` declaration ensures that nobody can instantiate a `Database` except for the `Database` itself. By checking to see if the `static instance` has been assigned before creating the instance, the class is able to ensure that only one copy is ever assigned. The pattern is extremely easy for other types to use, as they simply need to call `Database.getInstance()` to get an instantiated reference to the same instance of a `Database` as all other clients are using. Of course, this also highlights one of the key shortcomings of the Singleton: the Singleton itself acts as a global variable because every class in the system has the ability to get a reference to this type. This ease-of-access tends to lead to undisciplined use of Singleton types, leading to concrete references (because all client references must reference the static `getInstance()` method which must be declared on a concrete type). + +{{< figure src="dp_singleton.png" alt="singleton diagram" >}} + +## Decorator: Dynamically adding responsibilities to objects. + +The Decorator pattern is another structural pattern that provides a means to dynamically augment an object's responsibilities. With the decorator pattern it is important to distinguish between an _object_ and a _class_. A class is the structural template from which object instances are created. That is, an object is a single instance of a class and a class can have many different instances. Each object can have different field values, but the fields, methods, and parent types they have are all defined by the class they are instantiated from. + +The decorator pattern exists to add new responsibilities to _objects_, instead of to their whole _class_. This means that two objects instantiated from the same type can be modified at runtime to behave differently. Decorators work by enabling objects to be wrapped in other objects and using composition to treat the wrapped object as if it were a single object. + + + +For example, consider the following simple system where we can have a `Car` or three special versions of with additional features: + +{{< figure src="patterns_decorator-example_a.png" alt="decorator diagram" >}} + +One day a new customer asks for a car with both nav and adaptive cruise control. Planning ahead, the team realizes it is only a matter of time before customers ask for any subset of these features and set out to extend their design in the way that best preserves their existing design: + +{{< figure src="patterns_decorator-example_b.png" alt="decorator diagram" >}} + +While the above approach is conceptually consistent with the initial design, having seven subclasses of `Car` is not optimal and will surely cause extreme resistance to any new feature being added (for example `CarAutoLights`) as this will have to be mixed in with every existing subclass. Instead, the team decides to move to a system using a decorator, which enables a `Car` to be 'wrapped' in instances of `CarDecorator` to add additional features; this is great, because adding a new features means just adding a single extra class meaning the development team can go home for Christmas after all: + +{{< figure src="patterns_decorator-example_c.png" alt="decorator diagram" >}} + +It can be hard to visualize what this means from the class diagram alone. To create a version of a car with Nav and AutoBrake, one only needs to do the following: + +```typescript + let car = new Nav(new AutoBrake(new BaseCar()))); +``` + +Even at runtime this could allow for additional features. For instance: + +```typescript + // create car with Nav off + let car = new AutoBrake(new BaseCar())); + + // ... sometime later: + + // turn on Nav, wrap existing object + car = new Nav(car); +``` + + + +The decorator does have some downsides: it is impossible to control the 'order' of the wrappers with the pattern. This also means that the wrappers cannot interact with one another directly (e.g., above we could wrap a `BaseCar` with `Nav` twice, which doesn't make any sense). Also, decorator objects tend to be fairly small resulting in a large number of classes. Decorators also interfere with object identity, so code that relies on checking identity (e.g., with `instanceof`) will behave differently with wrapped and unwrapped objects. + +Ultimately the decorator pattern provides excellent support for maintaining the flexibility and extensibility of the system. Base classes can be kept simple focusing on their core responsibilities (single responsibility), while additional functionality can be implemented in decorators (open/close). This also means adding new decorators is easy and does not change the base classes. This is a textbook demonstration of the flexibility of composition over inheritance. + +## Composite: Consistent handling of part-whole relationships. + +Composites provide a mechanism for treating groups of objects the same as individual objects (often known as part-whole hierarchies). Systems often start with individual objects, but over time gain the ability to group objects together. Adding logic to differentiate individual objects from group objects adds unnecessary complexity to code. The composite pattern, through the composite (`Manager` in the example below) uses composition to maintain a list of children while still itself being the parent component type (`Employee` below). + +The introduction of the composite means any client can treat both managers and developers as employees (e.g., by asking for their names or ids uniformly), whether they have reports or not. This frees client code from checking if the `Employee` reference they have is a `Manager` or a `Developer`, and enabling a `Manager` to appropriately traverse all of their reports appropriately (even if some of their reports are themselves a `Manager`). + +{{< figure src="patterns_composite-example.png" alt="composite diagram" >}} + +In the example below, the default implementation of `Employee::getBudget()` would just be: + +```typescript +public getBudget():number { + return this.salary; +} +``` + +Meanwhile, the implementation of `Manager::getBudget()` would also capture the budget of their reports (some of whom could themselves be `Manager`s): + +```typescript +public getBudget():number { + let budget = this.salary; + for (const report of this.directReports) { + budget += report.getBudget(); + } + return budget; +} +``` + +But to the client whether an employee is a `Manager` or `Developer` would be totally transparent. + +```typescript +// employee 1233 has no reports +const e1 = getEmployee(1233); +Log.info(e1.getBudget()); + +// employee 1234 has 4 direct and 35 indirect reports +const e2 = getEmployee(1234); +Log.info(e2.getBudget()); +``` + + + + +## Visitor: Localizing data structure traversal. + +The visitor pattern enables operations to be performed on an object hierarchy without directly modifying the hierarchy itself (either by adding new classes or methods). The primary motivation for the pattern is that given a large set of objects it is often necessary to perform tasks on them that is not a part of their core responsibilities; this pollutes their classes and adds non-essential code to their classes that is spread across all classes. By providing an external mechanism for performing these tasks, the visitor extracts the code from the class hierarchy itself, while also bringing together all of the code for that task that would otherwise be spread across the object structure. + +The visitor does require one new method be added to every class in the structure being traversed, which is a method called `accept(visitor: Visitor): void`. While this is a change to the objects, all future visitors will work with this API, enabling additional visitors to be added transparently to the system. The pattern acknowledges that the tasks we want to perform _on_ a set of objects vary much more often than the core responsibilities of the objects themselves, so paying this one-time cost to enable future extensibility is often worthwhile. The `accept` method is responsible for managing the iteration over any of its child or composite components are called appropriately as well as for calling the visitor itself. One thing to note is that in languages that allow overloaded methods that vary only in parameter type, the `Visitor` implementations typically have multiple methods called `visit()` that only vary in the type of the parameter. This does not work in a language like TypeScript; usually methods will be called `visitFoo(..)` or `visitBar(..)`, etc. to differentiate between the different `visit` methods. + +For instance, in the diagram below one could imagine adding `numReports` or `topLangs` methods to `Manager` and `Developer`, but instead we have created a `TopLangsVisitor` and `NumReportsVisitor` which both traverse the hierarchy directly. Each `accept(v: Visitor)` method immediately calls `visitor.visit` which uses dynamic dispatch to call the right visitor method. The method within the visitor can then interrogate the provided object to retrieve the required information and maintain a running tally of the answer that can be reported after the traversal is complete (the visitor can accumulate state in its own fields). Note, `Manager::accept(Visitor)` would be slightly different (e.g., each object will ensure that its correct children (or composite components) are visited appropriately): + +```typescript +public accept(visitor: Visitor): void { + for (const report of this.directReports) { + report.accept(visitor); + } + visitor.visit(this); +} +``` + + +While adding new visitors is easy, adding new concrete types to the type hierarchy is hard. This is because every visitor needs a `visit` method for every type that is being traversed which could result in many visitors being impacted. Also, due to the runtime operation of the visitor being dictated by dynamic dispatch, it is often challenging to understand how the visitor works, if a problem is ever encountered. + + + +{{< figure src="patterns_visitor-example.png" alt="visitor diagram" >}} + + + + + +## References + +There are a vast set of resources about design patterns, the following are only a rough starting point: + +* Great overview of [most](https://sourcemaking.com/design_patterns) design patterns with concrete examples. + +* Repository of [many design patterns](https://github.com/torokmark/design_patterns_in_typescript) implemented in TypeScript. + +* Interesting article on language-specific support for [decorators](https://dzone.com/articles/is-inheritance-dead). + +* Nice [state pattern](http://gameprogrammingpatterns.com/state.html) article. + +* [Design Patterns: Elements of Object-Oriented Software](https://www.amazon.ca/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612) (Gang of Four Book). + +* [Head First Design Patterns](https://www.amazon.ca/Head-First-Design-Patterns-Brain-Friendly/dp/0596007124/ref=sr_1_1?ie=UTF8&qid=1541463656&sr=8-1&keywords=head-first+design+patterns) diff --git a/reader/content/low-level-design/design-patterns/dp_factory1.png b/reader/content/low-level-design/design-patterns/dp_factory1.png new file mode 100644 index 0000000000000000000000000000000000000000..9c648f72a034ba7fc0f52346634424ca95fb4c8a GIT binary patch literal 114031 zcmeFaWmuDaA25svqM)K8p-32rw1{+Ds7R-@AR;Zb(Kr=RNku|hk?xKGLnTDIJEU`? z2ODG0e_qCn>%Oo1)B8ThbA9kQ=60U{_;vGCSy65udh65uXNXX6}rFDnbRPKJm zH}|07{mWXaVpUb&VAv`PYm*}H8O~cqMhzsnA@%Z31;gzHPfB zr~UXmjgjDib*AiyAXYP0?9QOZFtR%Wu=!DWpKNj&tK@Q4*6HvUu`g3emdhW%PDt=R z{nV3n_jOgS>oWNcp0cJYcU-SLeQ@&ZTUA<#l^1J{hyC{Y`5j@9`Mh5RCwkymjGwXD z0ps2G4@91PS2==s34={gUubw62D=5@zjG&E{ZV+t@tqIvOYQtnAzGJk*;Ltt;njV} zq0yZrH!odxcvao{0YFZ~RQuW;1qBjL@Od}M4j*$8GVo~!_;(ciBOxJ;BmMRbl~df# zZ=cCL2|u>HIKWIoB13ZRlB~M(j`_hoWiK@bMHXYZPas*7i+#muPw*y~KV{^dP({A+ 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z|M>KO+o~{<5dZ%?x1Q(U&;6H-{%}} + +At runtime, MVC works as follows (for a user View update). + +1. The View notifies the Controller of the change. +1. The Controller modifies the Model as required. +1. The Model, which is the subject in the observer pattern, notifies all of its observers that it has changed. +1. The View, acting as the observer in the pattern, receives the notify event. Since MVC typically uses a pull-based observer pattern it then queries the Model object to understand the nature of the change and updates itself as required. + +Below is a simple MVC example that clarifies that multiple view options are available to the controller (although typically only one of them is bound at a time). + +{{< figure src="patterns_mvc-example.png" alt="mvc" >}} + +MVC has two primary drawbacks. + +1. Views tend to get out of control (MVC's derisive nickname is _MassiveViewController_) because it is often easiest to put functionality within the view (instead of in the Controller). This leads to huge views and anemic controllers. Heavy views are also harder to test than heavy controllers because view logic tends to get mixed up with display logic, forcing error-prone UI-based testing approaches to be used instead of more traditional unit tests which would typically be sufficient for the controller alone. +1. Every component talks to every other component. This is especially apparent with the Model: if it is meaningfully changed all components will need to be updated. This lack of isolation has a negative impact on the maintainability and extensibility of the system. + +That said, MVC does enable: + +1. Collaborative views; many views can render the same model in their own unique way. +1. Enhanced maintainability due to the ease with which new views can be added (if existing views have kept their logic in the Controller). +1. Team splitting by enabling designers, UX, and front-end teams to concentrate their work in the Views. +1. Enhanced testability by trying to isolate application logic within the controller. + +## MVP + +Model View Presenter (MVP) was popularized by Google and was heavily used within its GWT framework (and has subsequently been extended to many other web-based UI frameworks). The primary difference in MVP from MVC is that the Model is isolated from the View, and Views are more stringently minimal in MVP than in MVC to enhance testability and avoid heavyweight views. + +### Model + +The Model is relatively unchanged between MVP and MVC, except that it often uses an [event bus](https://github.com/google/guava/wiki/EventBusExplained) to communicate with the rest of the system. Using an EventBus allows for more fine-grained observation and makes it easier to debug update notifications at runtime. + +### View + +The view is significantly simpler in MVP than MVC. Most notably, the view _never_ sees Model objects. This means that the Presenter _must_ mediate all View/Model interactions. Another important difference is that the View only deals with primitive types (e.g., Number and String (and arrays of Number and String) in TypeScript). This is for two reasons: + +1. Since the View only sends/receives primitive types it is hard to put complex logic in the view, this ensures that the Presenter is the main location for all program logic. +1. Views become correspondingly similar and are often validated 'correct by inspection' since they are just rendering primitive types. +1. Finally, since the view only gets primitives, the Presenter has to mediate its interaction with the View using primitive types which are easy to ```assert``` or ```expect``` with traditional unit testing frameworks. + +### Presenter + +Presenters in MVP play the role Controllers were always meant to play in MVC: that is, they _actually_ contain all of the application logic. Presenters tend to be complex, but are easily unit tested given the nature of the communication path with the View. Together with a mock view, the Presenters can often be tested with traditional unit testing approaches without requiring a fake UI to be spun up (e.g., the MockView can just be a regular object, it does not need access to the DOM or any other window-specific features). + +The View and Presenter interfaces are tightly bound to each other; this is because each needs to know the public interface of the other to enable communication. + +```typescript +// sample view/presenter interface +public interface IView { + public setPresenter(p: IPresenter); + public showMain(); + + public interface IPresenter { + public onCancel(); + public onAction(action: String); + public createView(): IView; + } +} +``` + +{{< figure src="patterns_mvp.png" alt="mvp" >}} + +A more typical MVP design is shown below. In this example, ```ViewPresenter``` and ```OutlinePresenter``` each have one View associated with them (but this view could be set to any of the available options, depending on the runtime circumstance). Updates to the controller are optionally mediated by an ```EventBus```, while the ```AppController``` is responsible for dynamically starting and stopping the Presenters and configuring the View each Presenter is bound to (```AppController``` often listens to lifecycle event queues from the ```EventBus```). + +At runtime, MVP works as follows (for a user View update). + +1. The View notifies the Presenter of the change; the data passed between the View and Presenter consists only of method calls with primitive parameter types. +1. The Presenter modifies the Model as required. +1. The Model, which is the subject in the observer pattern notifies all of its observers that it has changed. +1. The EventBus (or the Presenter, if an EventBus is not being used), acting as the observer in the pattern, receives the notify event. +1. The Presenter determines how the View should be updated and refreshes it as required, although again only with primitive values. + +{{< figure src="patterns_mvp-example.png" alt="mvp" >}} + +Compared to MVC, MVP is more testable, and minimizes bloated views. Also while MVP still uses the observer to track changes to the Model, the EventBus makes it easier to keep track of updates within the system (as does the fact that the views are not receiving these updates directly). MVP's main downside is that the views tend to contain a lot of boilerplate primitive code that just updates UI widgets with values and calls methods within the Presenter in response to user actions. + +## MVVM + +Model View ViewModel is the most recent in this line of popular extensions to MVC. MVVM further cements the connection between the view and its controller by considering them a single unit. Unlike MVP though, the View typically uses [data binding](https://msdn.microsoft.com/en-us/library/ms752347(v=vs.110).aspx#Anchor_1) to allow data values to automatically update UI representations. This greatly decreases the amount of boilerplate code written by the developer. + +In MVVM the ViewModel acts more like the Presenter in MVP and contains most of the application logic for the system, where the ViewController instead focuses on preparing data in a way that is amenable for data binding (and responding to user-driven events appropriately). + +{{< figure src="patterns_mvvm.png" alt="mvvm" >}} + + + +## References + +* Short [overview](http://heim.ifi.uio.no/~trygver/themes/mvc/mvc-index.html) from the creator of MVC. + +* Succinct [rationale](http://www.teehanlax.com/blog/model-view-viewmodel-for-ios/) for MVVM. + +* MVP [document](http://www.wildcrest.com/Potel/Portfolio/mvp.pdf). + + diff --git a/reader/content/low-level-design/mv-star/patterns_mvc-example.png b/reader/content/low-level-design/mv-star/patterns_mvc-example.png new file mode 100644 index 0000000000000000000000000000000000000000..ec9f7ce7990dca19587cbd047e0c55faee867c19 GIT binary patch literal 532228 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The goal of all software processes is the same: to define the who, what, when, and how for enabling a project to succeed. + +These processes are really about enabling various stakeholders to communicate, discuss, and improve the overall system. Software systems involve a diverse set of stakeholders, including (but not limited to): + +* Developers +* QA +* Managers +* Customers +* Users +* Dev/Ops +* Support +* VPs/C-Suite + +Since these different stakeholders have different backgrounds, goals, constraints, and knowledge, a variety of different representations and documents are created to help them most effectively communicate. At a high level, each of these representations helps with one of four broad categories: specification, development, validation, and deployment/evolution. These include (again, not exhaustively): + +* Mission statements +* Business objectives +* Requirements +* System architecture +* Detailed design +* Implementation plan +* Test plan +* Risk assessments +* Deployment/Ops plan +* Maintenance strategy + +Not every project needs all of these documents. For example, a tiny system would have almost identical architectural and design documents. For isolated systems the test plan could include and subsume a formal risk assessment. But once diverse stakeholders are involved they can be extremely helpful. For example, having an architectural plan will make it much easier to develop and refine a deployment plan and provide a mechanism for system architects and the ops team to agree about large-scale layout of the system. Similarly, requirements documents are the fundamental bridge between the technical team and customers while also providing affordances for sales teams (e.g., to know what the product does), support (e.g., to know if the product is supposed to do), and testers (e.g., to develop a test plan for validating that the requirements are met). + +The core role of software process is to increase transparency about the software development process so all stakeholders know what is required of them and what they can expect from others. In this way, processes can help ameliorate risk by explicitly capturing stakeholder expectations and constraints. + +## Traditional approaches + +Waterfall is the 'classic' software development model. This model was adapted from previously-successful engineering processes. In waterfall, each project phase flows into the next with explicit stakeholder sign-off before the next phase begins. + +[//]: # (waterfall overview) +{{< figure src="process_waterfall.png" >}} + +Because the phases are distinct and linear, there are extremely clear hand-offs between the stakeholders involved in each adjacent step. Before a phase can be considered complete, an explicit set of exit criteria must be validated. The primary shortcoming of waterfall-based processes is that there is extreme pushback against revisiting a prior phase: once requirements are complete the requirements are fixed. In practice though, this is extremely limiting as requirements are often imperfectly understood at the outset of a project. Waterfall methods are also not flexible in the face of organizational change. Finally, the overall value of the system cannot be validated until the process has run to completion; this can be problematic for long-running projects. + + + +The Spiral model emerged in the 1980s to try to increase the responsiveness of waterfall-based processes. In contrast to Waterfall, the Spiral model revisits each system phase on each iteration. This process can be split into four main phases: + +* **Planning**: Gathering and analyzing requirements. +* **Risk analysis**: Identify sources of risk and mitigation strategies. +* **Engineering**: The system is built and validated. +* **Evaluation**: System is validated externally with customers to inform future iterations. + +Compared to Waterfall, the spiral model enables much greater sensitivity to changes to requirements than waterfall. Also, by keeping customers involved, the Spiral model can avoid situations where teams end up building the wrong piece of software. Unfortunately, the overhead of performing effective risk analysis and review can be overwhelming, and the duration between spirals (often over one year) result in large delays between customer feedback. + +## Agile approaches + + + +Agile approaches formally emerged in the early 2000s in response to concerns that development teams were not able to rapidly and flexibly produce software systems. One of the defining documents of this time was the [agile manifesto](http://agilemanifesto.org/) which can be summed up in four main tenets: + +* Individuals and interactions over processes and tools. +* Working software over comprehensive documentation. +* Customer collaboration over contract negotiation. +* Responding to change over following a plan. + +These principles are designed to foster increased customer interaction (and accountability) within a software system. The goal is to increase velocity by decreasing the amount of time developers spend building 'the wrong thing' and letting engineering teams try out different design alternatives to find what works best for the customer. The focus on experimentation also means the system is always in a buildable state meaning that agile approaches are often used by teams who need to frequently deploy their systems (e.g., using continuous deployment techniques). + +A wide variety of agile methodologies are practiced in industrial settings; these tend to follow a set of principles that work for the team using them, rather than blindly following a prescriptive set of rigid rules. We will briefly touch on three of these methodologies. + +### XP + +Extreme Programming (XP) was one of the first widely influential agile methodologies. XP mandated that systems should always be buildable and that developers should be willing to start small and adapt and refactor their systems as the need arose, rather than investing in huge up-front engineering efforts. Teams using XP follow five key principles: + +* **Communication**: Enabling open and continual communication between all stakeholders can help projects stay on track and make sure that all stakeholders are aware of the schedule impact various alternative decisions could have. +* **Simplicity**: By focusing on the simplest possible solution, engineers are able to validate their work with customers before tackling more expensive or challenging solutions. +* **Feedback**: Software is not developed in a vacuum, by combining feedback from tests, customers, and the team, more knowledge can be applied to ensure the most locally 'correct' decisions are being made. +* **Courage**: Be willing to discard failed experiments. An experiment is not a sunk-cost, but rather an opportunity to learn and improve the overall system. +* **Respect**: The focus on a system always being executable means that committing changes that 'break the build' is unacceptable as it slows down your teammates and hurts velocity. Respect also means focusing on the long-term understandability of your code by respecting the time a future developer would have to invest in understanding how your code works. + +### TDD + +Test-driven development (TDD) emerged from the agile movement as an approach to ensure that automated testing was always a key part of the development process. This focus on automated testing was important because agile approaches rely heavily on being able to quickly refactor a piece of code and refactoring is often risky if the end-state of a refactoring cannot be easily validated relative to the input state. + +TDD follows a simple linear set of steps: + +1. **Add a test:** the first step in TDD is always to write tests. By thinking about testing from the outset, developers are more likely to build [controllable and observable](Testing.md#testability) code. This also helps to shift emphasis from implementing the body of functions to their API signatures. +1. **Run the tests to ensure they fail:** While this step might seem silly, it is important to know, before you refactor, that the only failures in a test suite are the ones you expect (e.g., from the tests you just created for code that does not yet exist). +1. **Write the code/run the tests:** Next the code is written so that the tests can be made to pass. The emphasis in this phase is on the tests passing correctly, extraneous functionality is explicitly avoided (with the intuition being that if it was not extraneous, there would be a test for it). +1. **Refactor the code:** Since the developer now has a set of passing tests, they can refactor as needed to make improvements to the existing implementation that may be required from the knowledge gained while writing the code in the previous step. + +TDD is not often used in practice in its explicit form as developers have a hard time writing all their tests before starting development. That said, many experienced developers *do* write test skeletons before they start writing the implementations of their features as this can be a helpful way to ensure that the code provides the APIs needed to effectively validate the system. In addition to validation, this also ensures that *clients* will have access to the necessary API to use the new feature effectively. + +### Scrum + +[//]: # (scrum overview) +{{< figure src="process_scrum.png" >}} + +Scrum-derived methodologies comprise the most commonly-used agile processes applied in industry today. Scrum is an incremental iteration-based methodology that breaks work into fixed-length *sprints* (these range from one to three weeks). At the end of each sprint the code should be shippable, that is it is properly tested, but likely not complete. A product has a global set of requirements left to be implemented known as the *product backlog*. The work items being worked on in each sprint are called the *sprint backlog*. Scrum does not prescribe any specific engineering practices and instead defer to the team to choose those practices that best meet their specific needs. +Scrum-based team members also have several process-specific roles: + +* **Product owner**: This person defines product features and helps to prioritize features according to their value to the team. After each iteration the importance of various features can be re-evaluated. Establishing meaningful prioritization is an important role for the product owner to ensure that the team focuses their efforts on the most important tasks; this is particularly important during sprint planning. +* **Scrum Lead**: This is a single person that facilitates the scrum process; often these people have attended workshops to learn techniques for keeping teams on track. The scrum lead (a role formerly known as the scrum master) often works to shield the team from external interference and helps resolve problems that may be blocking the product. The Scrum lead is *not* the manager of the team. +* **Team**: Scrum-based teams are typically diverse and cross-functional containing designers, managers, engineers, clients, and test specialists. Teams are usually between 5 and 9 people. + +Scrum employs three broad ceremonies: + +* **Sprint planning**: This meeting takes place before the sprint and is an opportunity for the product owner to describe and motivate the highest priority user stories from the product backlog for the upcoming sprint (that will then comprise the sprint backlog). During this meeting the team takes the opportunity to probe for additional information so they can be sure they adequately understand the work that will be required. Once the product owner has specified their priorities, it is up to the technical team to build an estimate of how long each task will take, usually represented in 'points' although these are often analogous to hours of development time. The team then makes an explicit decision about which tasks will be a part of the sprint and which will be pushed to some future sprint. +* **Standup meeting**: During the sprint, teams meet daily for a *daily scrum* or *standup* meeting. These are held at the same time and place each day, usually in the morning, and are time-limited (usually 15 minutes or less). Only those team members that are committed, or are involved in the technical challenges of the system, get to speak during the scrum meeting, although others may attend. During this meeting each team member quickly outlines the challenges they are facing and anything that is blocking their progress for that day (some teams use a short script like 'What did you complete yesterday? What will you work on today? What is blocking your progress?', but this is not strictly required. These meetings are a great way for teammates who are working on technically interacting pieces of software to remain aware of what everyone on the team is working on and to help align work for the upcoming day to minimize teammates being blocked by each other. +* **Sprint review**: After each sprint, a review meeting is held for the team to demonstrate their features to the product owner or other stakeholders. This is possible because the output of each sprint is supposed to be a potentially-shippable product. These meetings are light-weight and are usually time limited to under two hours. The team also takes this opportunity to evaluate how much progress has been made on the sprint backlog. +* **Retrospective**: Every product and team is unique, so it is important that the team take time to reflect on how the sprint worked out. Usually this comes in the form of identifying activities the team should start doing, stop doing, or continue doing. This retrospective is a good opportunity to make sure the process is providing value for the team and to find opportunities to improve and refine the activities the team is performing. + +### Kanban + +Kanban is a pull-based software process framework that aims to emphasize continuous delivery and flexibility. To do this, Kanban encourages teams to think about the flow of work. Kanban teams make heavy use of agile boards to visualize and track work as it happens. While there is a lot of flexibility in how this board is structured, almost all teams have columns that at least capture the `backlog` (all available work), `doing` (the work that is currently being performed), `review` (work that is ready for sharing and review by the team), and `done` (work that has been reviewed and completed). While some teams may combine `doing` and `review`, splitting them enables better visibility into the completeness of the feature and enables an explicit touchpoint where multiple team members can consider whether the feature should be deployed immediately or combined with other features in the future. + +Rather than focus on an explicitly-scheduled iterative process as in Scrum, Kanban teams manage their work by setting WiP (Work in Progress) limits on the `doing` and `review` columns on their agile board; this is done to reduce the chances of the team being overwhelmed or work not getting pushed through to completion. While a strength of scrum relative to prior methodologies is that it drives teams to deliver smaller quanta of software (with the goal of being able to deliver after every sprint), Kanban tries to reduce this risk even further by further emphasizing the continuous development of features. In this way, any time an item moves to the `done` part of the agile board a Kanban-based team may ship the feature, without having to wait for a sprint to end. + +Every team must choose the process that works for them. Kanban arose from teams who felt that agile methodologies were helpful for their productivity and quality, but felt constrained by the rigid-feeling aspects of scrum with their explicit schedules, planning, and ceremonies. That said, Kanban and Scrum do have significant similarities as well with both leveraging daily standup meetings to ensure teams are always aware of how they are doing as well as relying on smaller units of work defined in user stories. + + + +## References + +* [Why](http://spectrum.ieee.org/computing/software/why-software-fails) software fails article. +* Interesting [critique](https://www.aaron-gray.com/a-criticism-of-scrum/) of scrum. + + diff --git a/reader/content/process/process_waterfall.png b/reader/content/process/process_waterfall.png new file mode 100644 index 0000000000000000000000000000000000000000..897afc94c3f4baca17bbf75ccf45e9fd28d68e03 GIT binary patch literal 40083 zcmZ^LWmr_*7cY!7A}9g^(j_1alF}$59nv*~bR#u%Du{?Q$k0egcMhp9(%p^H-7_@z zprZcoeJ&pk!jDm6lih_b#jfDZcS-L~12mC@a zdm{e?1*Iem_skF-_?y~DN?9HS#hnoa#rHJ|$_enwcLfE-nF9r7%>V^O;3EnOiG5=2 z3n5@2EelmmCrx=d0V6vbc0*&kmnQ74HugXd6ciy>0pO#JiIX9rah{nt8aY_lJ6YJ- zQeTa0_|neVNtl-QYN9_szt-twVfuF_TgTtq0yYS~`Ud=nodf)5Y@n;q)ms5Y2MZHm 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Peters and Tripp observe that, + +> ‘This paradox implies that one must solve a problem once to define it and then solve it again to create a solution that works.’ + +Software development is often considered a wicked problem because high-level software requirements can have important ramifications for the low-level implementation of the system (and vice versa). That is, without starting making implementation decisions one cannot fully understand the implications on the requirements themselves. + +Wicked problems often similar characteristics: + +* No definitive formulation. Given the diverse set of stakeholders involved in real software systems, each have their own set of priorities and constraints that will often be in contention with one another. +* No stopping rule. This is always true for software: systems are never fully 'done', bugs always exist, and new features are always possible. +* Solutions are not true-or-false, just good-or-bad. For most problems, no one software design is absolutely the best. Every software design has a set of benefits and tradeoffs. +* Solutions to wicked problems often only have one chance for success. Software systems are so expensive to produce that it is impractical to expect a team to generate multiple different complete solutions to see what works best. + +Fortunately, there is a path forward (if not a solution) for wicked problems and that is through interaction and discussion amongst stakeholders as observed by Mary Poppendieck, + +> ‘The appropriate way to tackle wicked problems is to discuss them. Consensus emerges through the process of laying out alternative understandings, competing interests, priorities, and constraints.’ + + + +Specifications are documents used to describe a software system. Specs connect customers and engineers and help to ensure that different parts of the implementation work together. A spec will define both what the system does and how the behaviour can be tested for correctness. Different specs are useful for different stakeholders (e.g., designers, developers, testers, managers, ops, customers...). + +At their core, all software specifications are abstractions that abstract away some kinds of details so stakeholders can focus on other details (e.g., Developers and Operations teams might collaborate on defining a deployment specification that specifically maps software elements to data centres). + +## Requirements + +There are two broad categories of requirements: + +* **Functional requirements:** These describe what the system must *do*. For example, "The system shall let the user logout with a single click" provides a specific feature the system must have. +* **Non-functional requirements:** These describe properties the system must *have* (often called quality attributes). For example, "The system should be usable" describes a property the system should exhibit. + +It is important to remember that requirements describe *what* a system is to do, but not *how* it is to be done. For example, a spec might specify that "a list should be sortable in 0(nlogn) time" but not that "a list will be sorted using quicksort". + +Non-functional properties play important roles in most systems. These include the 'ilities' like reliability, security, usability, complexity, evolvability, performance, etc. Like functional properties, these also often conflict with one another. For example, designing a system that has low complexity, high performance, and good usability is extremely challenging, while designing a system that has only two of those properties is significantly easier. By engaging stakeholders in discussions about requirements we can help them to understand which are most important for the successful operation of the system. + +That software failures are costly has been [well](http://spectrum.ieee.org/computing/software/why-software-fails) [described](http://www.cse.psu.edu/~gxt29/bug/softwarebug.html) [in](http://www.devtopics.com/20-famous-software-disasters/) [practice](http://www.tricentis.com/blog/2014/12/18/top-10-software-fails-of-2014/). One of the most common reasons behind these failures can be summarized as "specification failures". Because of this, correctly capturing and understanding requirements is fundamental for projects being successful; this is also why Agile methodologies incorporate customers onto development teams to try to decrease requirements misunderstandings. + +While there are many properties stakeholders should keep in mind when recording software specifications, these four are especially important. + +* **Complete:** A spec that does not completely describe the requirements provides opportunities for misunderstanding between stakeholders. +* **Consistent:** Conflicting requirements also generate misunderstandings that can make knowing the *right* behaviour impossible to understand. +* **Precise:** Natural language is inherently imprecise while source code must be precise for the computer to be able to interpret it. This disconnect, and the fact that specs cannot be automatically validated, means imprecise language can further complicate understanding intended behaviour. +* **Concise:** While the above points seem to say that "more is better" when it comes to specifications, having "more" also provides more space for imprecision and inconsistency to become problems. + +During the requirements elicitation process several different types of requirements can be gathered in addition to the functional and non-functional properties described above; these include: + +* Design constraints: Organizations typically have design constraints that must be imposed on software systems; these can include regulatory compliance, budget, languages and frameworks, and development/security process requirements. +* Environmental constraints: Software systems do not exist in isolation; environmental constraints ensure that the new system will work with existing systems. These constraints often the mandate execution environment (e.g., the operating system, library limitations, services that can be consumed and must be provided) and operational expectations (e.g., expected input, performance constraints, disaster recovery). +* Preferences: Not all requirements are equal, preferences provide a mechanism to explicitly rank requirements should they come in conflict. + +The process of finalizing a specification is inherently iterative as the various stakeholders gain greater understanding of interdependencies between requirements and technical limitations start to emerge. Building a spec can often be thought of as a pipeline process: + +[//]: # (scrum overview) +{{< figure src="specification_process.png" >}} + +During this process requirements are first elicited (gathered) from various project stakeholders. Next these requirements are modelled to gain a more concrete understanding of what is actually needed. If any further clarifications are needed here it is often quickest to go back to the stakeholder at this point. Once the requirements are understood they are recorded in a format all stakeholders can understand after which point the document can be validated for the four properties listed above. + +## Validating requirements + +Lord Kelvin said, + +> To measure is to know. If you can not measure it, you can not improve it. + +This sentiment has been strongly endorsed by modern software companies where common belief says that good decisions are always backed with meaningful metrics. Software systems themselves are no different: when a customer add a requirement to a software system, there is an expectation that that requirement will be delivered. To ensure this, it should be possible to validate that all requirements have been met, and that their implementation is correct. + +While for some requirements this can be easy (e.g., "the list should be sortable on both columns"), for others it can be impossible (e.g., "the system shall be performant"). In situations where validation is not easy (or even possible) the requirement is often restated in more concrete terms. For example for performance, concrete targets could be put on disk usage, peak memory usage, throughput rate, peak connected users, or any number of other _measurable_ metrics. + +Being able to validate a requirement is clearly advantageous for the customer, but it is also good for the technical team as it makes their "definition of done" extremely clear. In practice measurable objectives are usually achieved, if the objective was unrealistic it should have been caught during requirements elicitation rather than during project acceptance. Being concrete also ensures that stakeholders have consistent expectations about the final product. + + + +## References + +* A [reflection](https://blog.codinghorror.com/development-is-inherently-wicked/) on software being a wicked problem. + + diff --git a/reader/content/process/specifications/specification_process.png b/reader/content/process/specifications/specification_process.png new file mode 100644 index 0000000000000000000000000000000000000000..692da729352c2a9fed911c2cc5e4045c6b4e194b GIT binary patch literal 20004 zcmdRW_ajyR|2R@;7^x^TGvpeT?W*i~vm#uSm9p2hu1kevWh<*>WbbR=OW|gh&Be9F zwX!a*@jV)^_v_W?FZlL@&OPV(eC)^L`FK9h<8eanX(^vOedRO>3CTHC6$KpETJ0v6}k!SWFo&q2@hyc}Ev3_FJHvAYl&hX?AvY85c`y zDIEo+-_wC#vK)`y+?=F@gkUfj2zCSH=wc%zA}J{;BrGZu2puE;7csv${dpH)6?|Go=pV6xPiuVQ zeLzBTn?zONj-Ds!ay`|)pz?!x&AVJ&SFc`=x;4{%=`8JolUCnI?wq>%N^1J7RDsb<^&FLznBu zD5@V`)4c%BCMG+=!D!w7{tB6`<;BIq@h&>?Pj^VjX#GjZ+3#^Xg>ghAIPkFWvS25C z#xyfO#F(8%qztgd=|Lmhm9KTm9EA3m7<{eZ>zXX;baOv#FpWxgAJD&nF89qGfL?`H 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a/reader/content/process/user-stories/_index.md b/reader/content/process/user-stories/_index.md new file mode 100644 index 0000000..3fc2548 --- /dev/null +++ b/reader/content/process/user-stories/_index.md @@ -0,0 +1,59 @@ +--- +title: "User Stories" +--- + +User stories are lightweight descriptors of features that are often used by engineers to specify software development tasks. Stories provide a concrete description that can be used by various project stakeholders to discuss features, their value, how they can be validated, and their cost. These representations are an easy way to increase cohesion between product owners and engineers and are an effective way to reduce uncertainty within the development process. + +## Format + +User stories usually start with one to three sentences that describe the feature being built. The first sentence often takes the format: + +> Role - goal - benefit. + +This can be expanded to something more like: "As a `` I want `` so that ``." This format directly conveys who the feature is for, what it is to do, and what the value to the user is. + +The next part of the story is a series of notes that describes limitations and provides any clarifications that might be necessary to further explain and scope the feature. + +One crucial aspect of user stories is their _definition of done_. This provides a specific description of how the story can be validated by both the developer and the product owner to ensure it is completed correctly. In addition to helping the developer create a correct feature, it also helps them to avoid working unnecessarily on extra functionality the stakeholder might not need. + +Next, any specific engineering notes required can be added to the story. Engineering notes are often used to capture key interactions of the feature with other parts of the system, constraints the implementer must keep in mind, and other domain knowlege that may be required from other teams. These notes are often crucial to understand the effort required to implement a story. The Role-Goal-Benefit and Definition of Done are the two most important parts of a User Story and are written in the _problem domain_; that is, they are written from the client (and user's) perspective. They are specificaly NOT written in the _solution domain_, which is an engineering-forward view into how the story will be implemented, although the Engineering notes are always written from this perspective. + +Finally, an effort estimate is typically attached. While some development teams specifically avoid specifying effort in terms of hours or days, others do this explicitly. When 'points' are used, these are typically equated to developer-hours for a task. The Fibonacci sequence is often used when assigning a point value to stories to reflect the fact that the larger the story the worse we will probably be at estimated it. For instance, with the sequence `1, 2, 3, 5, 8, 13, 21, 34, 55, 89` we believe we can differentiate between a one-hour task and a two-hour task pretty well, but we acknowledge that differentiating between large stories that will take over a week (e.g., 55 or 89) is much harder, which is why larger gaps exist. _Planning poker_ is sometimes used whereby during point estimation team members will be given cards with the Fibonacci values on them and will simultaneously reveal their estimate of a task's point value; any discrepancies can then be discussed. + +A set of guidelines called INVEST is often used to evaluate user stories: + +* **I**ndependent: Stories need to be independent and self-contained, so they can be reordered and implemented as needed. +* **N**egotiable: Choosing which stories are performed in the next iteration needs to be a negotiable activity. This also allows the product owner and technical team to ask more questions that flesh out the story and provide greater detail as needed. +* **V**aluable: Being clear about the value of a story is important; most stories are not valuable to all stakeholders, but being able to have this conversation explicitly makes negotiating the relative benefits of various stories possible. +* **E**stimatable: If a story lacks sufficient detail to evaluate its feasibility (and how long it will take) more specification work is needed. One of the cornerstones of user stories is they make reasoning about tasks and maintaining schedules tractable; if a story cannot be estimated it will not work in practice. +* **S**mall: User stories tend to describe features that take between half a day and half an iteration in length. Longer stories should be split up to reduce the chance of a story not making it in an iteration and so that future work on the story can be negotiated in the subsequent work period. +* **T**estable: The definition of done is fundamentally important so all stakeholders understand the ways in which the story will be evaluated. This brings quality concerns to the forefront before development even starts and encourages that features be built in a verifiable manner. + +## Example + +A short example of a user story is given below: + +* As a _prof_, I want to be able to _create_ a repository for a 310 team, so they can _start_ working on the project. +* Notes: Inputs: team name, initial repo, team member GitHub ids. +* Definition of done: + * Single command will take params and complete task. + * Success should be programmatically verifiable. + * Unit tests to check for error handling for org, team name, and members. + * Integration test will ensure compatibility with the GitHub API. + * Script-based tests will be provided for the command line aspects of the feature. +* Engineering notes: + * Must integrate with existing ```GitHubManager``` + * Any constants that would need to be changed must be stored in ```config.js```. + * API will be used by user interface in the future, keep this in mind when designing API. +* Estimate: 1.5 units. + +User stories are not an excuse for writing unclear or incomplete descriptions of tasks; for example, the following are clearly not acceptable user stories: + +* Implement `TeamCreator`. +* Design team creation interface. +* Add extra tests. + +## References + +* TBD + diff --git a/reader/content/process/user-stories/process_scrum.png b/reader/content/process/user-stories/process_scrum.png new file mode 100644 index 0000000000000000000000000000000000000000..be7f48cfbfc436e20c886a75ee8614e701bf5c6d GIT binary patch literal 46823 zcmeFZbx>4&{{RXIvUDRL-Hi((DJ)1xcXxLqjf5a6-QC?OjdX)_NJw`|N$g$pd7fAN z|K6E9^JC61XU~4?(|I;XR$3GVkpK}23JOK+t<v)Dr_JDCkPSQ^=DeRclzt2Q{-d zZ)C;Zydjmfw=p)eG=hSnjWp2Jl^0{A?bFlK)$JQ*phL8GmJbXJmDg<>ZSQXHZ69p^ zqMMkYskwrOzVbw|11ekYOT7hhDCy5C=Dw2rrfcJzqjx-eEu&O6<&HbZlIOJE3{u$hcaNBdT&DwMyy=<-* zS5D+zFB$RhU>or9r1u%$GK#|@0iOUB)Crz+(&)OJJbm#3U}K=Wa;uPHYzZ4@vTmoZ ztJ`u32Ai-yeR|Q4jO;~o3kJJ?1%thwJz-zg)8&kShY|&wB!j~EwSFO4QPojZN|M{a 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Tests](http://www.youtube.com/watch?v=_Th3f9vks_w)) + +[//]: # ( * [Unit and System Properties](http://www.youtube.com/watch?v=x2DWjxDiOQo)) + +[//]: # ( * [Red Green Refactor](http://www.youtube.com/watch?v=v0q1MKhSQVM)) + +{{< youtube x2DWjxDiOQo >}} + +{{< youtube v0q1MKhSQVM >}} + + +Software systems are only useful if they do what they are supposed to do. This is increasingly important given the vital safety-critical roles modern software systems play. Unfortunately, proving that a system is correct is exceedingly difficult and expensive for large software systems. This leads to the fundamental tradeoff at the heart of most commonly applied testing approaches: + +> Given a finite amount of time and resources, how can we validate that the system has an acceptable risk of costly or dangerous defects. [Bob Binder] + +Given the constraints above, the most prevalent quality validation approach in use today is automated testing (followed by code reviews). Unfortunately, as Dijkstra noted in 1969: + +> Program testing can be used to show the presence of bugs, but never to show their absence! [Edsger Dijkstra] + +This is because what we are evaluating is whether the probability that there are no bugs in a system given that the tests pass is not actually 0. Since tests themselves are programs (and can have bugs) and specifications are often incomplete or imprecise we therefore must admit that the chance of a defect slipping through a set of tests is > 0 [(online discussion)](http://tonyxzt.blogspot.ca/2010/01/tests-can-show-presence-of-bugs-not.html). + +The modern test cycle is summarized below. The cycle between steps 1-7 can occur many times before the code is ready to commit. + +1. Develop some new code. +2. Deploy to testing environment. +3. Run your tests. +4. Test fails. +5. Try to fix the failure through more development. +6. Deploy to testing environment. +7. Run the tests again. +8. Test passes. +9. Commit/push change. +10. Pull new changes. + +Steps 2/7 may not happen on small teams or when testing happens solely on a single development machine. The code that is developed in steps 1/5 could involve either writing test or product code. In Test-Driven Development (TDD) the first develop step would be to write tests for features that haven't been written yet to guide future development. + +{{< figure src="test-cycle.png" >}} + +### Terminology + +{{< youtube WKrvx7qCUDI >}} + +A number of different terms are commonly used in the testing space: + +* ```SUT/CUT```: System / code under test. This is the thing that you are actually trying to validate. +* ```Glass box testing```: When testing in a glass box manner one typically carefully examines the program source code in order to identify potentially problematic sets of inputs or control flow paths. More details can be found in the [Glass box testing](GlassBoxTesting.md) reading. +* ```Black box testing```: Black box testing validates programs without any knowledge of how the system is implemented. This form of testing relies heavily on predicting problematic inputs by examining public API signatures and any available documentation for the CUT. More details can be found in the [Black box testing](BlackBoxTesting.md) reading. +* ```Effectiveness```: The simplest way to reason about the effectiveness of a test or test suite is to measure the probability the test will find a real fault (per unit of effort, which can be something like developer creation / maintenance time or number of test executions). +* ```Higher/lower testability```: Some systems are significantly easier to test than others due to the way they are constructed. A highly testable system will enable more effective tests for the same cost than a system whose tests are largely ineffective (or require an outsized amount of creation and maintenance effort). More details can be found in the [Testability](TestabilityAssertions.md) reading. +* ```Repeatability```: The likelihood that running the same test twice under the same conditions will yield the same result. + + +There are a number of different *levels* of test; these range in size, complexity, execution duration, repeatability, along with how easy they are to write, maintain, and debug. + +* ```Unit```: Unit tests exercise individual components, usually methods or functions, in isolation. This kind of testing is usually quick to write and the tests incur low maintenance effort since they touch such small parts of the system. They typically ensure that the unit fulfills its contract making test failures more straightforward to understand. +* ```Integration```: Integration tests exercise groups of components to ensure that their contained units interact correctly together. Integration tests touch much larger pieces of the system and are more prone to spurious failure. Since these tests validate many different units in concert, identifying the root-cause of a specific failure can be difficult. +* ```End-to-End```: Also referred to as *acceptance* testing, these tests typically validate entire customer flows. They are especially useful for validating quality attributes (such as performance and usability) that cannot be captured in isolation. These tests are great for ensuring that the system behaves correctly but provide little guidance for understanding why a failure arose or how it could be fixed. +* ```A|B```: A special subset of testing that is typically employed in production environments. In A|B testing, two different versions are compared at runtime to validate which one performs 'best'. A|B testing is often used to validate business decisions, rather than evaluate functional correctness. +* ```Smoke/Canary```: This is a subset of a test suite that typically executes quickly, is highly reliable, and has high effectiveness. Smoke tests try to expose a fault as quickly as possible, making it possible to defer running large swaths of unnecessary tests for a system that is known to be broken (for example, if you have an error in your data model that touches your whole system, there is no need to run slow integration and end-to-end tests). + +{{< figure src="test-levels.png" >}} + +For additional reference, take a look at this in-depth talk about how [Google tests](https://www.infoq.com/presentations/Continuous-Testing-Build-Cloud) their systems. + +### Why not test? + +There are a number of reasons why software systems are not tested with automated suites. These range from "bad design" to "slow", "boring", "doesn't catch bugs" and "that's QA's job". Ultimately, testing does have a cost: tests are programs too and take time to write, debug, and execute and must also be evolved along with the system. + +One core paradox of automated test suites is how they are used. If you think that a test only provides value when it fails by catching a bug that would have otherwise made it to production, then that all passing tests are just a waste of computational cycles. At the same time though, passing tests _do_ give us the warm feeling that we have not introduced unexpected regression bugs into our systems. But at the same time, these feelings only work if we believe our test suite is capable of finding faults and _could_ fail. Yes: this contradicts the 'we should only run failing tests' implication above. + +An area of interesting future research would be to figure out how often a suite needs to fail to impart trust, while also figuring out how few passing tests you could run to have a sense of confidence in your automated suite. + +### Common testing assumptions + +It has been long held that the cost of fixing a fault rises exponentially with how late in the development process (e.g., requirements, design, implementation, deployment) the fault is detected. This statement arises from several influential studies Barry Bohem performed in the 60s and 70s. A more in-depth description of these costs can be found [here](http://www.agilemodeling.com/essays/costOfChange.htm) or [here](http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20100036670.pdf). + +There is a more [modern discussion](http://www.sicpers.info/2012/09/an-apology-to-readers-of-test-driven-ios-development/) however that openly wonders if these costs are not relevant for more modern-styles of software development and programming languages. + +It is also important to accurately consider the costs of automated testing. Writing tests takes time. Executing tests takes time. Diagnosing failing tests take time. Fixing tests that were incorrect takes time (tests are programs too!). But catching real faults can save money, as can increased velocity enabled through better understanding how shippable your code is. While it is easier to account for the costs than the benefits, most large teams have invested heavily in automated test suites, although many large teams are actively investigating test prioritization and minimization schemes to reduce the delay between making a change and having a meaningful test result. + + + +### References + +* Another great talk about how [Google stores](https://www.youtube.com/watch?v=W71BTkUbdqE) its source code. + +* Steve Easterbrook [testing 1](http://www.cs.toronto.edu/~sme/CSC302/notes/16-Testing1.pdf). + +* Steve Easterbrook [testing 2](http://www.cs.toronto.edu/~sme/CSC302/notes/17-Testing2.pdf). + +* Laurie Williams [black box testing notes](https://students.cs.byu.edu/~cs340ta/spring2019/readings/BlackBox.pdf). + +* Laurie Williams [white box testing notes](https://students.cs.byu.edu/~cs340ta/spring2019/readings/WhiteBox.pdf). + +* Laurie Williams [automated testing notes](http://agile.csc.ncsu.edu/SEMaterials/AgileTesting.pdf) + +* MIT testing [slides](http://measure.cc.gt.atl.ga.us/classes/cs6300/Lectures/class18testing.pdf). + +* Jim Cordy's [quality assurance](http://research.cs.queensu.ca/home/joshuad/web327/Website/) course. + + + + + diff --git a/reader/content/testing/assertions/_index.md b/reader/content/testing/assertions/_index.md new file mode 100644 index 0000000..961d8ac --- /dev/null +++ b/reader/content/testing/assertions/_index.md @@ -0,0 +1,134 @@ +--- +title: "Assertions" +weight: 1 +--- + + +{{< youtube DCAFxgUXM4I >}} + +A key skill when creating automated unit tests is evaluating the _correctness_ of the code under test. While a compiler can validate the syntax of a program, a test suite is required to ensure its runtime behaviour matches its specification. Assertions are the primary piece of machinery used to evaluate correctness within automated unit test suites. Given that [jUnit](http://junit.org/) was one of the early (and still most popular) unit testing frameworks, it is unsurprising that many modern frameworks provide features similar to jUnit's [assertion features](https://github.com/junit-team/junit4/wiki/Assertions) for evaluating program outputs. + +There are several high-level models to consider when structuring tests; two of the most widely used are: + +* [Four-Phase Tests](http://xunitpatterns.com/Four%20Phase%20Test.html): This model is supported by default by jUnit and its variants. The four phases correspond to 1) setting up the testing environment; 2) executing the code under test; 3) evaluating the output of the CUT; and 4) tearing down the testing environment. + + + +* [Give-When-Then](https://github.com/cucumber/cucumber/wiki/Given-When-Then): This model was developed by the behavioural driven programming ([BDD](https://dannorth.net/introducing-bdd/)) community which strives to create 'executable specifications' through unit tests that use descriptive strategies for nesting and naming. Tests are structured from some _given_ state, where the system's configuration is understood. The key action of the test is the _when_; this is often described in the test name using plain language (e.g., ```it('should be able to parse a document that has UTF-16 characters')```). The _then_ step involves observing the output to ensure its correctness. Expect/Should-style assertions (such as those used by the [Chai library](http://chaijs.com/api/bdd/)) are also often used by the BDD community as they enable extremely descriptive assertion statements. + + +#### Chai's Expect/Should Assertions + +The Chai expect library provides a rich set of assertions that can be used to check for correctness. While traditional assertions are supported, e.g., + +```javascript +expect('myName').to.equal('myName'); +expect(1).not.to.equal(2); +``` + +Chai also includes support for more advanced checking: + +```javascript +expect(obj).to.deep.equal({ key: 'value' }); // check large object +expect(42).to.be.a('number'); // check type +expect([ 42, 97, 102 ]).to.have.length.above(1); // check array properties +expect([1, 2, 3]).not.to.include.members([1, 4]); // checks set membership +``` + +One benefit of the using the specific assertion forms above is that they easy to read and understand. For example, even without comments, the intent of the following Given-When-Then test doing is straightforward to comprehend: + +```javascript +describe('Check math constants', function() { + it('Math.PI should be close enough to the correct value', function() { + expect(Math.PI).to.be.closeTo(3.14, 0.025); + }); +}); +``` + +Additionally, assertion failures are easier to understand when specific assertion forms are used. For example, the following two assertions perform exactly the same check: + +```javascript +// Equivalent assertions +expect(arr).to.contain('c'); +expect(arr.indexOf('c') >= 0).to.equal(true); +``` + +But the error message for first assertion failing is more direct and understandable for the second. While this may not seem important for such a contrived example, for real, complex, failures more specific error messages can greatly simplify the process of understanding why an assertion failed. + +```javascript +// Corresponding failures +AssertionError: expected [ 'a', 'b' ] to include 'c' +AssertionError: expected false to equal true +``` + + + +It is often best to have each behaviour tested as independently as possible. This eases debugging because a change that breaks a behaviour will be easy to isolate as it will only have broken one test and not a handful. It also eases program evolution because changing a behaviour will not require modifications to lots of different tests. + +When creating our tests we can break down the behaviours we are testing in three ways: + +* Normal conditions: The normal conditions represent the way we expect the code to be used. +* Unexpected conditions: The unexpected conditions arise when the code is used in unexpected ways. +* Boundary conditions: All programs consume input; by testing boundary values we can ensure our program will successfully operate with the breadth of inputs the program might encounter. + +Ultimately each test should compare an expected value against the actual value produced by the code under test. One easy way to keep code easy to read is to make this explicit within the test, for example: + +```javascript +describe('Check math constants', function() { + it('Math.PI should be close enough to the correct value', function() { + const expected = 3.1415; + const actual = Math.PI; + expect(actual).to.be.closeTo(expected, 0.025); + }); +}); +``` + +{{< youtube Y0GEftJ5MB0 >}} + + +[//]: # (#### Assertions and async) +{{% notice tip %}} +Testing asynchronous functions can be challenging as asynchronous code often does not behave as expected at runtime. ***The [Async Cookbook](https://github.com/ubccpsc/310/blob/main/resources/readings/cookbooks/async.md#testAsynchronous) contains many examples of how to test asynchronous code.*** +{{% /notice %}} + + diff --git a/reader/content/testing/blackbox/_index.md b/reader/content/testing/blackbox/_index.md new file mode 100644 index 0000000..a95c4ed --- /dev/null +++ b/reader/content/testing/blackbox/_index.md @@ -0,0 +1,174 @@ +--- +linkTitle: "Black Box" +title: "Black Box Testing" +weight: 4 +--- + + +Black box testing approaches are used when the tester does not have (or is electing not to use) any knowledge about the implementation of the system under test. Instead of deriving test cases by examining the implementation as in glass box testing, in black box testing the tester uses the specification for the code under test and validates that the output is correct (according to the specification) for a given set of inputs. + +There are a variety of black box testing approaches that are used in practice: + +* **Boundary value analysis (BVA)**: Most functions can take an unbounded number of inputs. BVA seeks to identify which of those are most valuable to test. The intuition behind this process is that inputs at the *boundary* of an input domain are more likely to be problematic than those that are not. + + For example, when testing a numeric input that accepts values `1..10`, natural inputs outside the boundary would include `0` and `11` while natural inputs within the boundary could include `1` and `10`. BVA can also be applied to more complex types, such as dates with specified formats (e.g., `YYYY-MM-DD`). Similarly, BVA can also take place on objects. For example: + ```typescript + const user: { + username: string; + password: string; + } + ``` + Where `username` and `password` have specific restrictions on what inputs are valid. {{< youtube IRQU_fHKUa0 >}} + +* **Equivalence class partitioning (ECP)**: While testing at the boundaries is useful and often uncovers subtle defects, it is also useful to test more commonly expected inputs to ensure that the code under test is behaving as expected. As with boundary value analysis, equivalence class partitioning seeks to decrease the input space from all possible inputs to a reasonable subset of inputs. To do this, inputs are decomposed into *classes*, and testers ensure that at least one input from each class is validated. + + For the numeric function that takes a values `1..10` in the BVA paragraph above, the classes for this input would be `[n < 1, 1 < n < 10, n > 10]`. While there are similarities to BVA, the focus on ECP is not to test explicitly at the boundaries but to choose a reasonable value from each partition (e.g., `[-5, 5, 15]` for this example). Usually testers will consider values using both ECP and BVA simultaneously to ensure broad coverage of the input space without making a strong distinction between the two. Additional detail about the two most common ECP strategies are given below in [Input partitioning](#input) and [Output partitioning](#output). {{< youtube ttAAsNSPiXw >}} + +* **State transition testing**: This kind of black box testing requires the tester to identify the states, or modes, the program will assume at runtime, determine how the program flows between these states, and then test that the program behaves as expected during these transitions. + + For example, imagine a system for an electronic lock. Such as system would have two natural states: `open` and `closed`. The only valid transition from `closed->open` is through the provisioning of a valid credential, while `open->closed` may happen when the door is closed or after some timeout. In state transition testing, each behaviour is validated through explicit test cases. + +* **User acceptance testing**: Finally, use case testing / user story testing is a form of black box testing that tries, as much as possible, to simulate user interaction with the system. While use case testing often validates the *happy path* where the most common and expected inputs are validated to ensure the system behaves as expected. Use case tests though can also simulate both *untrained and adversarial* users to try to exercise the system in ways these users might as well in an effort to ensure both expected and unexpected use cases are handled in a way that makes sense from a user's perspective. + + Use case testing is similar to a customer validating a user story by evaluating the definitions of done for a story in a sprint review. In this way use case testing and user story testing form a kind of user acceptance test. + + +## Input partitioning + +{{< youtube 5PtxXnwyU3Y >}} + +Input partitioning is a black box testing technique that strives to find value partitions for a function based on the inputs for the function. + +The main idea behind input partitioning is to group the input space of a function into equivalence classes, which are distinct sets of inputs derived from the specification of the function. These groups are usually chosen based on the function's input parameters. We then ensure that each equivalence class is validated at least once. A few examples of input partitioning are shown below: + +### Example 1 + +```typescript +/** + * Returns the PNE ticket price for the corresponding age. + * + * REQUIRES: age is a natural number + * EFFECTS: if age < 5, return 0 + * if age >= 5 and age < 19, return 20 + * if age >= 19 and age < 65, return 40 + * if age >= 65, return 15 + */ +function getTicketPrice(age: number): number { + // implementation hidden +} +``` + +Testing the entire input space for the function above is unreasonable, given that there are an unbounded number of natural numbers to evaluate. Instead, input partitioning can be used to decompose the input space according to the specification. There are 4 groups of natural numbers that the specification defines, and the equivalence classes can be generated as 0-4, 5-18, +19-64, and 65+. A comprehensive test suite for these equivalence classes would have a test case that tests a value +between 0 and 4, 5 and 18, 19 and 64, and a value greater than 65 to ensure the correct value is returned for each. + +### Example 2 + +```typescript +/** + * Returns the square root if the input number is a perfect square. + * Throws a NotPerfectSquareError if input number is negative or is not a perfect square. + */ +function getPerfectSquareRoot(input: number): number { + // implementation hidden +} +``` + +Examining the specifications above, we can partition the input space into three equivalence classes: positive numbers that are +a perfect squares, positive numbers that are not perfect squares, and negative numbers. An example test suite would be the test cases 16 (positive perfect square), 10 (positive number that is not a perfect square), and -5 (negative number). + +### Example 3 + +```typescript +enum Player { + A, B +} + +/** + * Returns whether the given player is allowed to move with the given speed. + * Player A can only have a positive speed (moving right) and + * Player B can only have a negative speed (moving left). + */ +function isValidDirection(player: Player, speed: number): boolean { + // implementation hidden +} +``` + +Creating equivalence classes for functions with multiple inputs requires combining the classes for each individual input. There are 2 input classes for `player` (A, B) and 2 classes for `speed` (positive number, negative +number). The final step is combining the sets to arrive at four test cases, as each input class is assigned another input class. The test cases {A, positive number}, {A, negative number}, {B, positive number}, {B, negative number} are obtained for this example function, covering the input partitions. + +### Strengths + +* Input partitioning is a black box testing technique, which means that the implementation of the function is not required to validate its behaviour. Not only does this make this technique more flexible, but it can also avoid confirmation biases that can occur when constructing test cases for code you authored or have viewed. +* Although input partitioning is a black box approach, it provides a systematic means to derive inputs that are likely to correspond to different code blocks within an implementation. +* For many functions, the input space could be very large, or even infinite. Input partitioning simplifies these large domains into a more approachable set of inputs. + +### Caveats + +* Input partitioning is dependent on the specification. If the specification is incomplete or the implementation deviates from the specification, the inputs chosen by input partitioning may miss important cases that should be tested. +* Sometimes the inputs to a function are less interesting than the outputs; in these cases, output partitioning may be a better choice. +* Defects often arise at the boundaries of partitions. Input partitioning is often combined with boundary value analysis to cover these cases more comprehensively. + + +## Output partitioning + +{{< youtube 0yvHDKI-DSA >}} + +Similar to input partitioning, output partitioning provides a mechanism for a developer to systematically explore a domain of inputs for a function. In contrast to input partitioning though, these inputs are driven by the outputs of a function. Although input partitioning is more commonly used than output space partitioning, output partitioning allows the tester to analyze the function from a different perspective, which is often useful when the output of a function is more interesting than its inputs. + +### Example 1 + +```typescript +/** + * Turns a number of seconds into a 24 hour time format. + * + * REQUIRES: 0 <= seconds < 86400 + * + * EFFECTS: returns a string of the format "HH:MM:SS:, where H,M,S are digits. + * if H==0, return string format "MM:SS" + * if H==0 && M==0, return string format "SS" + */ +function timeAfterSeconds(seconds: number): string { + // implementation hidden +} +``` + +The input specification for this function is relatively straightforward (e.g., there is one partition as the number of seconds is bound by the REQUIRES clause) resulting in single input partition [0, 86399]. In contrast, there are several output partitions: + +* Output string has format "HH:MM:SS" +* Output string has format "MM:SS" +* Output string has format "SS" + +To test these three output partitions, the tester needs to derive inputs to achieve the given output; this extra step is one reason why output partitioning is slightly more complicated than input partitioning (although this step is trivial for this example). + +### Example 2 + +```typescript +/** + * Given a start time and a finish time (both in seconds), return the speed of a 100 meter short distance run. Throw an exception if startTime < finishTime. + * + * REQUIRES: startTime, finishTime >= 0 + * + */ +function speedOfShortDistance(startTime: number, finishTime, number): number { + // implementation hidden +} +``` + +The inputs to these functions are just both non-negative numbers. But the outputs add the constraint about `finishTime` being later than `startTime`. While this could result in two output partitions, one valid and one testing this extra output constraint, it would not be unreasonable to also validate the case where `startTime` and `finishTime` are equal. Although this _should_ be treated the same as the already-validated error case, it would certainly be worth validating. NOTE: combining output partitioning with boundary value analysis would have also surfaced this condition. + +### Strengths + +* The greatest strength of output partitioning is that it focuses tests on observable outputs. This is a strength because users of a function expect the kinds of outputs they observe to be correct. +* Output partitioning can encourage a developer to approach their tests in a different way than input partitioning alone which can lead to a better diversity of test cases while still staying within a reasonable number of test cases. + +### Caveats + +* Functions usually have fewer outputs than inputs (simplistically a function has a single output value and can have many input parameters). This usually drives testers to explore 'easier' input space more thoroughly than the output space (although it should be noted that the complexity of the output space is often underestimated, and the 'single' return value could in fact be a complex object or side effecting operation). +* The effort required to derive inputs to achieve a given output is often challenging. This can be especially observed in erroneous situations (e.g., an output behaviour dictates how a system should behave when disk space is exhausted, but actually testing this can be hard). + + +--- +[![](figures/CCSA.png "Creative Commons: Attribution-ShareAlike")](https://creativecommons.org/licenses/by-sa/3.0/) [Reid Holmes](https://www.cs.ubc.ca/~rtholmes/) + + diff --git a/reader/content/testing/fuzz/_index.md b/reader/content/testing/fuzz/_index.md new file mode 100644 index 0000000..fef8844 --- /dev/null +++ b/reader/content/testing/fuzz/_index.md @@ -0,0 +1,44 @@ +--- +linkTitle: "Fuzz" +title: "Fuzz Testing" +--- + +Fuzz testing is a testing approach that seeks to automate some aspects of the test *writing* process. +Fuzz testing concerns itself with programmatically generating inputs to a system/code under test (SUT/CUT) in a way that uses some elements of randomness. Some fuzzing strategies are black-box. This means that they require no information about the SUT. Most advanced fuzzing strategies, however, are grey-box or glass-box. These strategies use feedback about the execution of the SUT, such as code coverage, to improve their input generation process. + +Fuzzing is often used to test a simple specification of the SUT: that the system does not crash. That is, fuzzing is used to find segmentation faults or other types of runtime errors. Additional specifications are necessary for fuzzing to check more advanced types of correctness properties, such as whether or not the SUT computes the right value. + +At the high level, fuzzing (1) generates an input, (2) executes the SUT with the generated input, (3) observes if the SUT has crashed, and (4) if the SUT did not crash, fuzzing goes back to step (1). + +In what follows we describe three black-box fuzzing approaches: random fuzzing, generator-based fuzzing, and mutational fuzzing. + +## Random fuzzing + +Random fuzzing involves choosing inputs completely at random. Usually the SUT requires that the input is of a particular type, like a string or an integer. In this case, random fuzzing will choose a well-typed random input. + +An advantage of random fuzzing is that it is simple to implement. Also, random fuzzing can generate unexpected inputs -- those that exercise paths in the code that are unlikely to be exercised in practice by users. + +A disadvantage of random fuzzing is that the generated inputs may be *too* random. For example, when fuzzing a program like an XML parser, random fuzzing is unlikely to generate strings that are valid XML. As a result, on programs that require highly structured inputs (like XML), random fuzzing is unlikely to reach code logic beyond the input validation stage. + +## Generator-based fuzzing + +Generator-based fuzzing is an approach to improve on random fuzzing. This approach requires that a developer writes an input generator that generates inputs that are (1) random, and (2) valid for the SUT. + +For example, to test an XML parser, the developer can write a generator to output random XML structures. The resulting XML structures may have random tags, random amount of nesting, etc. But, because they would be valid XML, they would exercise more new paths in the XML parser than would be exercised with random fuzzing. + +A challenge with generator-based fuzzing is that a developer must write code for an effective input generator. If the developer writes a generator that is overly constrained and generates a limited number of inputs, then fuzzing with the generator would have limited reach in the SUT. + +More broadly, generator-based fuzzing is an example of an input/test generation technique that relies on knowing the input specifications of the SUT. Other techniques in this space include property-based testing and grammar-based fuzzing. For example, property-based testing is like generator-based fuzzing, but typically with richer assertions in the SUT. + + +## Mutational fuzzing + +Mutational fuzzing is another approach to improve on random fuzzing. With mutational fuzzing a developer defines *mutations*, which are operations that transform one input into another, slightly different input. For string-like inputs, a set of standard mutations can be applied, such as insert-char, delete-char, and duplicate-char. These mutations can then be applied to a seed input that is a valid input to a SUT to generate more inputs that are both valid and invalid (e.g., valid XML and invalid XML inputs). + +For example, given a seed string like ```f```, a duplicate-char mutation may produce a string like ```f``` (invalid XML). On the other hand, an insert-char mutation may produce a string like ```ff``` (valid XML). + +A challenge with mutational fuzzing is selecting a seed input to mutate. This seed is often an example of an input that a user would provide, or it is an input extracted from a human-written test. + +Another challenge with mutational fuzzing is defining the set of mutations and a policy to determine whether and how often mutations can be applied on top of one another. + +Note: be careful and do not confuse mutational fuzzing with mutation testing. These are unrelated concepts. \ No newline at end of file diff --git a/reader/content/testing/glassbox/_index.md b/reader/content/testing/glassbox/_index.md new file mode 100644 index 0000000..4c34b1f --- /dev/null +++ b/reader/content/testing/glassbox/_index.md @@ -0,0 +1,95 @@ +--- +linkTitle: "Glass Box" +title: "Glass Box Testing" +weight: 3 +--- + +Most common glass box testing approaches leverage coverage to help engineers understand which portions of their systems have or have not been tested. While glass box testing approaches are more commonly used than black box approaches, they can suffer from _confirmation bias_ in that by looking at an implementation, you are more likely to make similar assumptions as the author of the code being tested and miss important test cases. This bias can be helpful though if you are striving for high-coverage in a system you wish to be a reliable indicator for a regression suite as it enables you to be more confident that you are testing all behaviours currently handled by an implementation to ensure they continue to behave similairly as the code evolves. + +## Coverage + +{{< youtube iujQEm9oono >}} + +When evaluating the quality of the system we really want insight into how well the system performs its expected tasks and how robustly it responds to unexpected inputs and situations. Unfortunately, measuring this kind of property meaningfully is impossible, so developers often ask a simpler question: + +> Does the test suite test the whole system? + +Since the system code is supposed to both perform the expected operations and handle unexpected ones, coverage can make sure we are testing both of these aspects of the system. At its core, all coverage tools measure the proportion of the system that is executed by the test suite. Coverage has two properties in particular that make it appealing: + +1. It is relatively cheap to compute. In contrast to reasoning about expected and unexpected behaviours, a coverage tool simply needs to track which parts of a program have executed when the test suite is executing. + +1. It is actionable. Developers can look at the output of a coverage report, find the parts that are not currently covered by the test suite, and decide how to act upon this information in a straightforward way. + +There are many flavours of coverage that are used in practice, here we describe four of them: + +* **Line**: Line coverage measures the fraction of lines of code that are executed by a set of test cases. When calculating line coverage the denominator reflects _executable_ lines; these include function definitions (for parameter assignment), and implicit returns (if applicable) while excluding whitespace, comments, and lines containing only charters that do not actually execute (e.g., braces). + {{< expand title="Example" >}} {{% youtube SC7R-QixRaU %}} {{< /expand >}} + +* **Statement**: This measures the fraction of the statements of the code that are executed. Statement coverage is slightly stronger than line coverage in that it requires all statements on a line to be executed to satisfy the coverage condition. + +* **Branch**: Branch coverage ensures that both conditions of all branch points are executed by the test suite. For example, branch coverage measures whether both true and false branches of `if` statements are executed by the test suite. + +* **Path**: A more stringent form of coverage ensures that all program _paths_ are exercised. This almost always requires more test cases than branch coverage as we need to evaluate not only the true/false branches of every conditional, but also every combination of paths between all conditional outcomes. + {{< expand title="Example" >}} {{% youtube YBiHn5vgKnM %}} {{< /expand >}} + +For example, given the test and code snippet below: + +```typescript { lineNos="true" } +function eval(x: number, c1: boolean, c2: boolean): number { + if (c1) + x++; + if (c2) + x--; + return x; +} +``` + +And the following test: + +```typescript +eval(0, false, false); +``` + +The code has 67% line and statement coverage (lines 1, 2, 4, 6 are covered while lines 3 and 5 are not), 50% branch coverage (only the false branches of the `if` statements on lines 2 and 4 are covered), and only 25% of possible program paths are covered. The following suites would provide 100% coverage for each of the different coverage criteria: + +```typescript +// 100% line coverage +eval(0, true, true); + +// 100% statement coverage +eval(0, true, true); + +// 100% branch coverage +eval(0, true, true); +eval(0, false, false); + +// 100% path coverage +eval(0, true, true); +eval(0, false, false); +eval(0, true, false); +eval(0, false, true); +``` + +### Summary + +{{< youtube 2EBi66DYF5w >}} + +The stronger the coverage criteria, the more stringently we can say the code was covered by the test suite. The greatest shortcoming of coverage however is that while it clearly shows that the suite _executes_ the code under test, it does not show that the code under test is _correct_. This can be clearly seen by our tests above, none of them check that the output from `eval` is correct for the inputs given. It is extremely important to keep this shortcoming in mind when thinking about coverage because ultimately we really do care about the correctness of the system over how comprehensively a test suite might execute it. This is one of the main reasons why defects are frequently found in code that is covered by the test suite: specifying and checking assertions of correctness on code is often harder than deriving the inputs required to cover it in the first place. + + + + + +### References + + +* It can be easy to over-rely on coverage as a measure of test effectiveness. Brian Marick has written a great essay on [How To Misuse Code Coverage](http://www.exampler.com/testing-com/writings/coverage.pdf) that talks about many of these pitfalls. + +* Jo Atlee has a long-form talk on [detecting and resolving software errors]( +https://livestream.com/itmsstudio/events/6919492/videos/147823325?t=1485444595837) that provides additional insight into how coverage is just one piece of a comprehensive testing strategy. + +* Walmart Labs has a nice post on how to interpret coverage reports to [address coverage shortcomings](https://medium.com/walmartlabs/do-you-have-100-code-coverage-10c09a44832b) with straightforward code examples. + diff --git a/reader/content/testing/mutation/_index.md b/reader/content/testing/mutation/_index.md new file mode 100644 index 0000000..5473429 --- /dev/null +++ b/reader/content/testing/mutation/_index.md @@ -0,0 +1,20 @@ +--- +linkTitle: "Mutation" +title: "Mutation Testing" +--- + +Mutation Testing is an advanced form of software testing that is used to interrogate the sensitivity of a test suite. The core idea behind mutation testing is that if a test suite is sensitive to errors, it should be able to detect intentionally-introduced changes to the source code under test. + +Mutation operators are used to create mutant versions of the code under test. Mutants tend to be simple: for example changing bounds checks (e.g., `a >= arr.length` to `a > arr.length`), or boolean values (e.g., `if (isReady) {}` to `if (!isReady) {}`). Mutation testing frameworks are able to generate tens of thousands of mutants on any program; the main problem in mutation testing is not _generating_ mutants but in generating mutants that are likely to fail. This is because each mutant needs to be evaluated independently. The implication of this is that if you have 1,000 mutants and 1,000 test cases you must run 1,000,000 test cases to evaluate every mutant for every test case. + +The core idea behind mutation testing is known as the _coupling hypothesis_ that posits that human-introduced faults are coupled (similar) to the kinds of mutants that can be programmatically generated by a mutation testing tool. While it may feel unsatisfying that the complex faults a person could introduce could be replicated by simple mutants, this has been [demonstrated to be true](https://www.cs.ubc.ca/~rtholmes/papers/fse_2014_just.pdf). + +The main process of mutation testing involves: + +1. Run the test suite and ensure all tests pass on an un-mutated version of the system. +1. Perform the following loops for many mutants: + 1. Introduce a single mutation into the program. + 1. Run the test suite again and see if any tests fail. If a test fails, the mutant is said to be killed. If no tests fails, the mutation is said to survive. +1. The mutation kill score of a test suite is expressed as the fraction of mutants that are killed: `kill score = # mutants killed / (# mutants killed + # mutants survived)` + +The main goal of mutation testing is to provide insight into the quality of a test suite. 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Testability is a quality attribute that does not affect how the system performs its functional requirements, but instead influences how amenable the system itself is to being tested. + +In order to be successful, a test needs to be able to execute the code you wish to test, in a way that can trigger a defect that will propagate an incorrect result to a program point where it can be checked against the expected behaviour. From this we can derive four high-level properties required for effective test writing and execution. These are controllability, observability, isolateablilty, and automatability. + +It is often necessary to restructure software that has not been written in a testable way to make it possible to validate a system effectively; this commonly happens when units within the code take on more than one responsibility or when features become scattered across a codebase. One of the biggest advantages of Test-Driven Development (TDD) is that by writing your tests first you are able to ensure that your system is structured in a testable way. + +### Controllability + +{{< youtube y0zrTdDuYv0 >}} + +Tests dynamically execute the system. If the system/code under test (SUT/CUT) cannot be programmatically controlled, an automated test cannot be written for it. There is often a controllability tradeoff between what code it is *possible* to write a test for and what code it is *efficient* to write a test for. For example, if you build a large system that embeds all of its logic tightly with its interface it can be impractical to validate how the system works without writing UI-tests, which are error-prone and expensive. In practice, one of the most common mechanisms for improving the controllability of code is to add additional parameters to methods or constructors, both to enable dependencies to be more easily varied and to allow more expressive or complete inputs to be sent to a method. + +### Observability + +{{< youtube CJhJibMCTUM >}} + +Sometimes the CUT can be invoked by a test but its outcome cannot be observed. For example, if a defective method mutates some external inaccessible object but does not return a value. Often these can be resolved by returning data to the caller that might otherwise just been passed further down a call chain. One surprising challenge when considering observability is determining what values are correct and what values are erroneous. While this sounds simple, this is the root cause of the old saying "That's not a bug, it's a feature!"; at their core these are often caused by imprecise, incomplete, or contradictory specifications. + +### Isolateability + +{{< youtube Z93-c4ngxGw >}} + +{{< youtube NMuhE-XnFe8 >}} + +Being able to isolate a fault within the code under test is crucial to be able to quickly determine what has caused a failure so it can be resolved. This is challenging in large modern systems due to the number of (often third party) dependencies software systems have. For example, if a data access routine fails is it the logic in the routine or is it a failure in the underlying database? At its simplest level, isolateability can be increased by decomposing larger functions into smaller more self-contained functions that can be tested independently. + +Sometimes code will have complex dependencies, requiring isolation through simulation. In simulation-based environments code dependencies are _mocked_ or _stubbed_ whereby they are replaced with developer-created fake components that take known inputs and return known values (e.g., a ```MockLoginRejectController```) would always return false for a ```login(user, pass)``` without needing to check a user store, database, or external system. In this way the developer can test their code that uses ```login(..)``` and ensure it handles the false case correctly without fear that a bug in the real login controller may return an incorrect or inconsistent value. In addition to isolation, mocking also greatly increases performance and makes components less prone to non-determinism as the result being returned is usually fixed and not dependent on some external complex computation. Mocking can also make it possible to test program states that would otherwise be hard to trigger in practice (for instance if you want to test a situation where a remote service is down you can have a ```MockTimeoutService``` that just does not respond to requests). + +### Automatability + +{{< youtube Q83W5zH8LUY >}} + +The above properties all aim to support making it possible to automate the execution of the test suite. Suites that can run without human intervention are much more likely to be executed than those that must be manually performed. They are also more likely to be run after a system has been released in the form of a regression suite to ensure that a system continues to execute as expected. All systems exist within organizations, even if the software does not change, external dependencies, libraries, frameworks, and computing infrastructure will which will require continuous validation of even deployed systems. The economic benefits or automation are also clear: even if it takes 5 hours to configure an automated test case that can be performed in 30 minutes, the suite will break-even (in terms of time) after only 10 test iterations. Automated suites can be run online as changes are made to a system, after every commit, nightly, or weekly, depending on the needs of the system. These suites also provide global visibility of the state of the system; if the suite has been 'green' for a week a team might feel more comfortable about deploying it than for a system whose suite has been failing for the same period of time. + +### Summary + +{{< youtube d2WjviF50HA >}} + +The four properties of testability are not commonly considered on their own. While observability and isolatability are the two properties that matter the most (e.g., can I _detect_ that a fault exists, and if so, _where_ exactly in my code is it), these properties are not enough in practice. If you have an isolateable unit, but you cannot control it (which usually means being able to pass in key dependencies (e.g., by dependency inversion) or values (e.g., input/output partitioning)) to trigger the fault, then being isolated does not matter, because you cannot trigger the behaviour you want to observe. + +Consequently, isolateability without controllability is not very useful. Similarly, controllability alone also is not enough because large flexible functions may enable many specific behaviours to be triggered, while still being hard localize failures within due to their size or the scale of the external dependencies such a large function may invoke. Automatability is the least commonly considered testability property because automatability could be considered shorthand for code that is _programmatically controllable_. + +More details about testability can be found in [Bob Binder's](http://robertvbinder.com/wp-content/uploads/rvb-pdf/talks/GTAC-2010-Binder-Testability.pdf) GTAC [talk](https://www.youtube.com/watch?v=1keyEiJHqPw). [Miško Hevery](https://www.youtube.com/watch?v=XcT4yYu_TTs) has a cool walkthrough video of live coding a refactoring to enable a simple system to be more testable. A nice collection of [testability links](http://stackoverflow.com/a/2085988/3133691) have been compiled on StackOverflow. + + +### Resources + +* Concrete example showing how to increase [isolation](http://anzorb.com/unit-tests-102-isolating-your-code-with-mocks-and-stubs/) with mocks and stubs. + +* Some best and worst [practices](http://blog.stevensanderson.com/2009/08/24/writing-great-unit-tests-best-and-worst-practises/) for writing unit tests. + +* Great article on [testability](https://www.toptal.com/qa/how-to-write-testable-code-and-why-it-matters). +

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