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% Encoding: UTF-8
% Publishers
@String{ACM = "ACM"}
@String{IEEE = "IEEE"}
% Conferences
@String{ICSE = "Proceedings of the International Conference on Software Engineering (ICSE)"}
@String{ICST = "Proceedings of the International Conference on Software Testing, Verification and Validation (ICST)"}
@String{ASE = "Proceedings of the International Conference on Automated Software Engineering (ASE)"}
@String{CommACM = "Communications of the ACM"}
@String{ESE = "Empirical Software Engineering (ESE)"}
@String{ESEM = "Proceedings of the International Symposium on Empirical Software Engineering and Measurement (ESEM)"}
@String{FSE = "Proceedings of the Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE)"}
@String{HICSS = "Proceedings of the Hawaii International Conference System Sciences (HICSS)"}
@String{ICC = "Proceedings of the IEEE International Conference on Communications (ICC)"}
@String{ASPEC = "Proceedings of the Asia-Pacific Conference on Software Engineering (ASPEC)"}
@String{SPLC = "Proceedings of the ACM International Systems and Software Product Line Conference (SPLC)"}
@String{ISCI = "Proceedings of the IEEE International Scientific Conference on Informatics (ISCI)"}
@String{ICPC = "Proceedings of the International Conference on Program Comprehension (ICPC)"}
% ICSM became ICSME somewhen...
@String{ICSM = "Proceedings of the International Conference on Software Maintenance (ICSM)"}
@String{ICSME = "Proceedings of the International Conference on Software Maintenance and Evolution (ICSME)"}
@String{ICPE = "Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE)"}
@String{ISSRE = "Proceedings of the International Symposium on Software Reliability Engineering (ISSRE)"}
% Journals
@String{IST = "Journal of Information and Software Technology (IST)"}
@String{JASE = "Automated Software Engineering (ASE)"}
@String{JSS = "Journal of Systems and Software (JSS)"}
@String{MSR = "Proceedings of the Conference on Mining Software Repositories (MSR)"}
@String{OOPSLA = "Proceedings of the Conference on Object-oriented Programming Systems and Applications (OOPLSA)"}
@String{QSIC = "Proceedings of the International Conference on Quality Software (QSIC)"}
@String(SEAMS = "Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)")
@String{SoSyM = "Software and Systems Modeling (SoSyM)"}
@String{TSE = "IEEE Transactions on Software Engineering (TSE)"}
@String{WOSP = "Proceedings of the First International Workshop on Software and Performance (WOSP)"}
@String{SIGPLANNOT = "ACM SIGPLAN Notices"}
@inproceedings{eggerTimeBandwidthNarrowing2012,
author = {S. {Egger} and P. {Reichl} and T. {Hoßfeld} and
R. {Schatz}},
booktitle = ICC,
month = jun,
pages = {1325-1330},
title = {{Time is bandwidth? Narrowing the gap between
subjective time perception and Quality of
Experience}},
volume = {},
year = {2012},
publisher = IEEE
}
@article{nahStudyTolerableWaiting2004,
author = {Nah, Fiona Fui-Hoon},
journal = {Behaviour \& Information Technology},
month = may,
number = {3},
pages = {153-163},
title = {{A Study on Tolerable Waiting Time: How Long Are
{{Web}} Users Willing to Wait?}},
volume = {23},
year = {2004},
publisher = IEEE,
language = {en},
}
@book{molyneauxArtApplicationPerformance2015,
address = {Beijing},
author = {Molyneaux, Ian},
edition = {2nd},
publisher = {{O'Reilly}},
series = {Theory in Practice},
title = {The Art of Application Performance Testing},
year = {2015},
}
@article{breivoldSystematicReviewSoftware2012,
address = {Newton, MA, USA},
author = {Breivold, Hongyu Pei and Crnkovic, Ivica and
Larsson, Magnus},
journal = IST,
month = jan,
number = {1},
pages = {16-40},
publisher = {Butterworth-Heinemann},
title = {{A Systematic Review of Software Architecture
Evolution Research}},
volume = {54},
year = {2012},
issn = {0950-5849},
}
@article{guoGeneticAlgorithmOptimized2011,
address = {New York, NY, USA},
author = {Guo, Jianmei and White, Jules and Wang, Guangxin and
Li, Jian and Wang, Yinglin},
journal = JSS,
number = {12},
pages = {2208--2221},
publisher = {Elsevier Science Inc.},
title = {{A Genetic Algorithm for Optimized Feature Selection
with Resource Constraints in Software Product Lines}},
volume = {84},
year = {2011},
issn = {0164-1212},
doi= "10.1016/j.jss.2011.06.026",
}
@inproceedings{vokolosPerformanceTestingSoftware1998,
author = {Vokolos, Filippos I. and Weyuker, Elaine J.},
booktitle = WOSP,
pages = {80-87},
publisher = ACM,
title = {{Performance Testing of Software Systems}},
year = {1998},
language = {en},
}
@inproceedings{reicheltHowDetectPerformance2018,
author = {Reichelt, David Georg and K\"uhne, Stefan},
booktitle = ICPE,
pages = {183-188},
publisher = ACM,
title = {How to {{Detect Performance Changes}} in {{Software
History}}: {{Performance Analysis}} of {{Software
System Versions}}},
year = {2018},
language = {en},
}
@inproceedings{fooMiningPerformanceRegression2010,
author = {Foo, King Chun and Jiang, Zhen Ming and Adams, Bram and
Hassan, Ahmed E. and Zou, Ying and Flora, Parminder},
booktitle = QSIC,
pages = {32-41},
publisher = IEEE,
title = {{Mining Performance Regression Testing Repositories
for Automated Performance Analysis}},
year = {2010},
}
@inproceedings{pintoAutomatingAssessmentPerformance2015,
author = {Pinto, Felipe and Kulesza, Uira and Silva, Leo and
Guerra, Eduardo},
booktitle = HICSS,
pages = {5144-5153},
publisher = IEEE,
title = {Automating the {{Assessment}} of the {{Performance
Quality Attribute}} for {{Evolving Software
Systems}}: {{An Exploratory Study}}},
year = {2015},
}
@article{bulej2005,
author = {Bulej, Lubom\'{\i}r and Kalibera, Tom\'{a}\v{s} and
Tma, Petr},
journal = {Performance Evaluation},
month = may,
number = {1-4},
pages = {345--358},
publisher = {Elsevier Science Publishers B. V.},
title = {{Repeated Results Analysis for Middleware Regression
Benchmarking}},
volume = {60},
year = {2005},
issn = {0166-5316},
}
@article{lee2012,
author = {Lee, Donghun and Cha, Sang K. and Lee, Arthur H.},
journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
number = {8},
pages = {1345--1360},
publisher = IEEE,
title = {{A Performance Anomaly Detection and Analysis
Framework for DBMS Development}},
volume = {24},
year = {2012},
month = aug,
}
@inproceedings{hegerAutomatedRootCause2013,
author = {Heger, Christoph and Happe, Jens and
Farahbod, Roozbeh},
booktitle = ICPE,
pages = {27--38},
publisher = ACM,
title = {{Automated Root Cause Isolation of Performance
Regressions During Software Development}},
year = {2013},
}
@inproceedings{nguyenIndustrialCaseStudy2014,
author = {Nguyen, Thanh H. D. and Nagappan, Meiyappan and
Hassan, Ahmed E. and Nasser, Mohamed and
Flora, Parminder},
booktitle = MSR,
pages = {232-241},
publisher = ACM,
title = {{An Industrial Case Study of Automatically Identifying
Performance Regression-Causes}},
year = {2014},
doi = {10.1145/2597073.2597092},
isbn = {978-1-4503-2863-0},
language = {en},
}
@book{fullerIntroductionStatisticalTime1996,
author = {Fuller, Wayne A.},
edition = {2nd},
publisher = {{Wiley}},
series = {Wiley Series in Probability and Statistics},
title = {Introduction to Statistical Time Series},
year = {1996},
isbn = {978-0-471-55239-0},
language = {en},
}
@article{pageContinuousInspectionSchemes2,
author = {Page, Ewan S.},
journal = {Biometrika},
number = {1/2},
pages = {pages 100-115},
publisher = {JSTOR},
title = {{Continuous Inspection Schemes}},
volume = {41},
year = {1954},
month = jun
}
@book{gustafssonAdaptiveFilteringChange2001,
author = {Gustafsson, Fredrik},
month = oct,
publisher = {{John Wiley \& Sons, Ltd}},
title = {Adaptive {{Filtering}} and {{Change Detection}}},
year = {2001},
doi = {10.1002/0470841613},
isbn = {978-0-470-84161-7 978-0-471-49287-0},
language = {en},
}
@techreport{settlesActiveLearningLiterature2010,
author = {Settles, Burr},
institution = {{University of Wisconsin, Madison}},
number = {1648},
title = {{Active Learning Literature Survey}},
year = {2010},
language = {en},
}
@book{rasmussenGaussianProcessesMachine2006,
author = {Williams, Christopher KI and Rasmussen, Carl Edward},
number = {3},
publisher = {MIT Press Cambridge},
title = {{Gaussian processes for machine learning}},
volume = {2},
year = {2006},
}
@phdthesis{duvenaudAutomaticModelConstruction2014,
author = {Duvenaud, David},
month = nov,
school = {University of Cambridge},
type = {{Doctoral Thesis}},
title = {{Automatic Model Construction with Gaussian
Processes}},
year = {2014}
}
@article{robertsGaussianProcessesTimeseries2012,
author = {Roberts, Stephen and Osborne, Michael and Ebden, Mark and
Reece, Steven and Gibson, Neale and Aigrain, Suzanne},
journal = {Philosophical Transactions of the Royal Society A:
Mathematical, Physical and Engineering Sciences},
number = {1984},
pages = {20110550},
publisher = {The Royal Society Publishing},
title = {{Gaussian Processes for time-series modelling}},
volume = {371},
year = {2013},
}
@inproceedings{siegmundPerformanceinfluenceModelsHighly2015,
author = {Siegmund, Norbert and Grebhahn, Alexander and
Apel, Sven and K\"astner, Christian},
booktitle = FSE,
pages = {284--294},
publisher = ACM,
title = {{Performance-Influence Models for Highly Configurable
Systems}},
year = {2015},
doi="10.1145/2786805.2786845",
}
@article{plac,
author = {Burman, J. and Plackett, R.},
journal = {Biometrika},
month = jan,
pages = {305-325},
title = {{The Design of Optimum Multifactorial Experiments}},
volume = {33},
year = {1946},
}
@inproceedings{kaltenecker_distance-based_2019,
author = {Kaltenecker, Christian and Grebhahn, Alexander and Siegmund, Norbert and Guo, Jianmei and Apel, Sven},
title = {{Distance-based Sampling of Software Configuration Spaces}},
booktitle = ICSE,
year = {2019},
pages = {1084--1094},
publisher = IEEE,
doi="10.1109/ICSE.2019.00112",
}
@inproceedings{nguyenUsingControlCharts2012,
address = {Montreal, QC, Canada},
author = {Nguyen, Thanh H.D.},
booktitle = ICST,
month = apr,
pages = {491-494},
publisher = IEEE,
title = {Using {{Control Charts}} for {{Detecting}} and
{{Understanding Performance Regressions}} in {{Large
Software}}},
year = {2012},
abstract = {Load testing is a very important step in testing of
large-scale software systems. For example, studies
found that users are likely to abandon an online
transaction if the web application fails to response
within eight seconds. Performance load tests ensure
that performance counters such as response time stays
in the acceptable range after each change to the
code. Analyzing load tests results to detect
performance regression is very time consuming due to
the large amount of performance counters.},
doi = {10.1109/ICST.2012.133},
isbn = {978-0-7695-4670-4 978-1-4577-1906-6},
language = {en},
}
@inproceedings{nguyenAutomatedDetectionPerformance2012,
author = {Nguyen, Thanh H.D. and Adams, Bram and
Jiang, Zhen Ming and Hassan, Ahmed E. and
Nasser, Mohamed and Flora, Parminder},
booktitle = ICPE,
pages = {299},
publisher = ACM,
title = {{Automated Detection of Performance Regressions Using
Statistical Process Control Techniques}},
year = {2012},
language = {en},
}
@inproceedings{malikAutomaticDetectionPerformance2013,
author = {Malik, Haroon and Hemmati, Hadi and Hassan, Ahmed E.},
booktitle = ICSE,
pages = {1012-1021},
publisher = IEEE,
title = {{Automatic Detection of Performance Deviations in the
Load Testing of Large Scale Systems}},
year = {2013},
}
@inproceedings{georgesStatisticallyRigorousJava,
author = {Georges, Andy and Buytaert, Dries and Eeckhout, Lieven},
title = {{Statistically Rigorous Java Performance Evaluation}},
booktitle = OOPSLA,
year = {2007},
pages = {57--76},
publisher = ACM,
}
@inproceedings{fooIndustrialCaseStudy2015,
author = {Foo, King Chun and Jiang, Zhen Ming and Adams, Bram and
Hassan, Ahmed E. and Zou, Ying and Flora, Parminder},
booktitle = ICSE,
month = may,
pages = {159-168},
publisher = {{IEEE}},
title = {An {{Industrial Case Study}} on the {{Automated
Detection}} of {{Performance Regressions}} in
{{Heterogeneous Environments}}},
year = {2015},
language = {en},
}
@article{garnettSequentialBayesianPrediction2010,
address = {Oxford, UK},
author = {Garnett, Roman and Osborne, Michael A. and
Reece, Steven and Rogers, Alex and
Roberts, Stephen J.},
journal = {The Computer Journal},
number = {9},
pages = {1430--1446},
publisher = {Oxford University Press},
title = {{Sequential Bayesian Prediction in the Presence of
Changepoints and Faults}},
volume = {53},
year = {2010},
month= nov
}
@article{osborneRealtimeInformationProcessing2012,
address = {New York, NY, USA},
author = {Osborne, Michael A. and Roberts, Stephen J. and
Rogers, Alex and Jennings, Nicholas R.},
journal = {ACM Transactions on Sensor Networks (TOSN)},
month = nov,
number = {1},
pages = {1--32},
publisher = {ACM},
title = {{Real-time Information Processing of Environmental
Sensor Network Data Using Bayesian Gaussian
Processes}},
volume = {9},
year = {2012},
issn = {1550-4859},
}
@inproceedings{guoVariabilityawarePerformancePrediction2013,
author = {Guo, Jianmei and Czarnecki, Krzysztof and Apely, Sven and
Siegmund, Norbert and Wasowski, Andrzej},
booktitle = ASE,
pages = {301-311},
publisher = IEEE,
title = {{Variability-aware Performance Prediction: A
Statistical Learning Approach}},
year = {2013},
isbn = {978-1-4799-0215-6},
doi = "10.1109/ASE.2013.6693089",
}
@inproceedings{sarkarCostEfficientSamplingPerformance,
author = {Sarkar, Atri and Guo, Jianmei and Siegmund, Norbert and
Apel, Sven and Czarnecki, Krzysztof},
booktitle = ASE,
pages = {342--352},
publisher = IEEE,
title = {{Cost-Efficient Sampling for Performance Prediction
of Configurable Systems}},
year = {2015},
doi="10.1109/ASE.2015.45",
}
@inproceedings{nairUsingBadLearners2017,
author = {Nair, Vivek and Menzies, Tim and Siegmund, Norbert and
Apel, Sven},
booktitle = FSE,
pages = {257-267},
publisher = ACM,
title = {{Using Bad Learners to Find Good Configurations}},
year = {2017},
doi="10.1145/3106237.3106238",
}
@inproceedings{haDeepPerf2019,
author = {Ha, Huong and Zhang, Hongyu},
booktitle = ICSE,
pages = {1095--1106},
publisher = IEEE,
title = {{DeepPerf: Performance Prediction for Configurable
Software with Deep Sparse Neural Network}},
year = {2019},
doi="10.1109/ICSE.2019.00113",
}
@inproceedings{siegmundPredictingPerformanceAutomated2012,
author = {Siegmund, Norbert and Kolesnikov, Sergiy and
Kästner, Christian and Apel, Sven and Batory, Don and
Rosenmüller, Marko and Saake, Gunter},
booktitle = ICSE,
pages = {167-177},
publisher = IEEE,
title = {{Predicting Performance via Automated
Feature-Interaction Detection}},
year = {2012},
doi="10.1109/ICSE.2012.6227196"
}
@ARTICLE{nairFlash18,
author={Vivek {Nair} and Zhe {Yu} and Tim. {Menzies} and Norbert {Siegmund} and Sven {Apel}},
journal=TSE,
title={Finding Faster Configurations Using FLASH},
publisher=IEEE,
year={2020},
volume={46},
number={7},
pages={794-811},
doi="10.1109/TSE.2018.2870895"
}
@inproceedings{ohFindingNearoptimalConfigurations2017,
author = {Oh, Jeho and Batory, Don and Myers, Margaret and
Siegmund, Norbert},
booktitle = FSE,
pages = {61-71},
publisher = ACM,
title = {{Finding Near-Optimal Configurations in Product Lines
by Random Sampling}},
year = {2017},
doi={10.1145/3106237.3106273}
}
@inproceedings{cityIdentifying2014,
author = {J{\"{u}}rgen Cito and Dritan Suljoti and Philipp Leitner and Schahram Dustdar},
title = {{Identifying Root Causes of Web Performance Degradation Using Changepoint Analysis}},
booktitle = {Proceedings of the International Conference on Web Engineering (ICWE)},
publisher = {Springer},
pages = {181--199},
year = {2014},
doi = "10.5167/uzh-96073"
}
@inproceedings{zamanQualitativeStudyPerformance2012,
title = {A Qualitative Study on Performance Bugs},
booktitle = MSR,
publisher = IEEE,
author = {Zaman, S. and Adams, B. and Hassan, A. E.},
month = jun,
year = {2012},
pages = {199-208},
}
@inproceedings{muehlbauerAccurate,
title = {Accurate Modeling of Performance Histories for Evolving Software Systems},
booktitle = ASE,
publisher = IEEE,
author = {Mühlbauer, Stefan and Apel, Sven and Siegmund, Norbert},
month = nov,
year = {2019},
pages = {to appear},
}
@article{laaber2019,
author = {Laaber, Christoph and Scheuner, Joel and Leitner, Philipp},
title = {Software Microbenchmarking in the Cloud. How Bad is It Really?},
year = {2019},
issue_date = {August 2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {24},
number = {4},
issn = {1382-3256},
url = {https://doi.org/10.1007/s10664-019-09681-1},
doi = {10.1007/s10664-019-09681-1},
journal = {Empirical Softw. Engg.},
month = aug,
pages = {2469–2508},
numpages = {40},
keywords = {Cloud, Performance testing, Performance-regression detection, Microbenchmarking}
}
@inproceedings{casalicchio2017,
author = {Casalicchio, Emiliano and Perciballi, Vanessa},
title = {Measuring Docker Performance: What a Mess!!!},
year = {2017},
isbn = {9781450348997},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3053600.3053605},
doi = {10.1145/3053600.3053605},
booktitle = {Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion},
pages = {11–16},
numpages = {6},
keywords = {container, internet of service, docker, microservices, cloud computing, monitoring, performance evaluation},
location = {L'Aquila, Italy},
series = {ICPE ’17 Companion}
}
@article{ousterhout_always_2018,
author = {Ousterhout, John},
title = {Always Measure One Level Deeper},
year = {2018},
volume = {61},
publisher = ACM,
journal = {{Communications of the {ACM}}},
pages = {74–83},
doi="10.1145/3213770"
}
@article{2019industry,
title={Industry Paper: The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System},
author={Daly, David and Brown, William and Ingo, Henrik and O’Leary, Jim and Bradford, David},
year={2019}
}
@inproceedings{hill_which_2013,
author = {Hill, Emily and Bacchelli, Alberto and Binkley, Dave and Dit, Bogdan and Lawrie, Dawn and Oliveto, Rocco},
title = {Which Feature Location Technique is Better?},
year = {2013},
booktitle = ICSME,
publisher = IEEE,
pages = {408–411},
doi="10.1109/ICSM.2013.59",
}
@inproceedings{alves_sampling_2020,
author = {Pereira, Juliana Alves and Acher, Mathieu and Martin, Hugo and J\'{e}z\'{e}quel, Jean-Marc},
title = {Sampling Effect on Performance Prediction of Configurable Systems: A Case Study},
year = {2020},
publisher = ACM,
booktitle = ICPE,
pages = {277–288},
doi="10.1145/3358960.3379137",
}
@inproceedings{rabkin_static_2011,
author = {Rabkin, Ariel and Katz, Randy},
title = {Static Extraction of Program Configuration Options},
year = {2011},
publisher = ACM,
booktitle = ICSE,
pages = {131–140},
doi="10.1145/1985793.1985812"
}
@inproceedings{han_empirical_2016,
author = {Han, Xue and Yu, Tingting},
title = {An {{Empirical Study}} on {{Performance Bugs}} for {{Highly Configurable Software Systems}}},
booktitle = ESEM,
year = {2016},
pages = {1--10},
publisher = ACM,
doi = {10.1145/2961111.2962602},
}
@article{guo_genetic_2011,
title = {A Genetic Algorithm for Optimized Feature Selection with Resource Constraints in Software Product Lines},
author = {Guo, Jianmei and White, Jules and Wang, Guangxin and Li, Jian and Wang, Yinglin},
year = {2011},
volume = {84},
pages = {2208--2221},
journal = JSS,
number = {12}
}
@inproceedings{muhlbauer_accurate_2019,
title = {Accurate {{Modeling}} of {{Performance Histories}} for {{Evolving Software Systems}}},
booktitle = ASE,
author = {M{\"u}hlbauer, Stefan and Apel, Sven and Siegmund, Norbert},
year = {2019},
month = nov,
pages = {640--652},
publisher = {{IEEE}},
doi="10.1109/ASE.2019.00065",
}
@inproceedings{siegmund_performance-influence_2015,
title = {Performance-Influence Models for Highly Configurable Systems},
booktitle = {Proceedings of the 2015 10th {{Joint Meeting}} on {{Foundations}} of {{Software Engineering}} - {{ESEC}}/{{FSE}} 2015},
author = {Siegmund, Norbert and Grebhahn, Alexander and Apel, Sven and K{\"a}stner, Christian},
year = {2015},
pages = {284--294},
publisher = {{ACM Press}},
address = {{Bergamo, Italy}},
doi = {10.1145/2786805.2786845},
abstract = {Almost every complex software system today is configurable. While configurability has many benefits, it challenges performance prediction, optimization, and debugging. Often, the influences of individual configuration options on performance are unknown. Worse, configuration options may interact, giving rise to a configuration space of possibly exponential size. Addressing this challenge, we propose an approach that derives a performance-influence model for a given configurable system, describing all relevant influences of configuration options and their interactions. Our approach combines machine-learning and sampling heuristics in a novel way. It improves over standard techniques in that it (1) represents influences of options and their interactions explicitly (which eases debugging), (2) smoothly integrates binary and numeric configuration options for the first time, (3) incorporates domain knowledge, if available (which eases learning and increases accuracy), (4) considers complex constraints among options, and (5) systematically reduces the solution space to a tractable size. A series of experiments demonstrates the feasibility of our approach in terms of the accuracy of the models learned as well as the accuracy of the performance predictions one can make with them.},
file = {/home/stefan/Zotero/storage/39HPPJTU/Siegmund et al. - 2015 - Performance-influence models for highly configurab.pdf},
isbn = {978-1-4503-3675-8},
language = {en}
}
@inproceedings{siegmund_predicting_2012,
title = {Predicting Performance via Automated Feature-Interaction Detection},
booktitle = {2012 34th {{International Conference}} on {{Software Engineering}} ({{ICSE}})},
author = {Siegmund, Norbert and Kolesnikov, Sergiy S. and K{\"a}stner, Christian and Apel, Sven and Batory, Don and Rosenmüller, Marko and Saake, Gunter},
year = {2012},
month = jun,
pages = {167--177},
publisher = {{IEEE}},
address = {{Zurich}},
doi = {10.1109/ICSE.2012.6227196},
abstract = {Customizable programs and program families provide user-selectable features to allow users to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. Our work aims at predicting program performance based on selected features. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. We present a method that automatically detects performance-relevant feature interactions to improve prediction accuracy. To this end, we propose three heuristics to reduce the number of measurements required to detect interactions. Our evaluation consists of six real-world case studies from varying domains (e.g., databases, encoding libraries, and web servers) using different configuration techniques (e.g., configuration files and preprocessor flags). Results show an average prediction accuracy of 95 \%.},
file = {/home/stefan/Zotero/storage/UY8XPAZD/Siegmund et al. - 2012 - Predicting performance via automated feature-inter.pdf},
isbn = {978-1-4673-1066-6 978-1-4673-1067-3},
language = {en}
}
@article{sarkar_cost-efcient_nodate,
title = {Cost-{{Efficient Sampling}} for {{Performance Prediction}} of {{Configurable Systems}}},
author = {Sarkar, Atri and Guo, Jianmei and Siegmund, Norbert and Apel, Sven and Czarnecki, Krzysztof},
abstract = {A key challenge of the development and maintenance of configurable systems is to predict the performance of individual system variants based on the features selected. It is usually infeasible to measure the performance of all possible variants, due to feature combinatorics. Previous approaches predict performance based on small samples of measured variants, but it is still open how to dynamically determine an ideal sample that balances prediction accuracy and measurement effort. In this paper, we adapt two widely-used sampling strategies for performance prediction to the domain of configurable systems and evaluate them in terms of sampling cost, which considers prediction accuracy and measurement effort simultaneously. To generate an initial sample, we introduce a new heuristic based on feature frequencies and compare it to a traditional method based on t-way feature coverage. We conduct experiments on six realworld systems and provide guidelines for stakeholders to predict performance by sampling.},
file = {/home/stefan/Zotero/storage/2DLHSB8T/Sarkar et al. - Cost-Efficient Sampling for Performance Prediction .pdf},
language = {en}
}
@inproceedings{guo__2013,
title = {Variability-Aware Performance Prediction: {{A}} Statistical Learning Approach},
shorttitle = {Variability-Aware Performance Prediction},
booktitle = {2013 28th {{IEEE}}/{{ACM International Conference}} on {{Automated Software Engineering}} ({{ASE}})},
author = {Guo, Jianmei and Czarnecki, Krzysztof and Apel, Sven and Siegmund, Norbert and Wasowski, Andrzej},
year = {2013},
month = nov,
pages = {301--311},
publisher = {{IEEE}},
address = {{Silicon Valley, CA, USA}},
doi = {10.1109/ASE.2013.6693089},
abstract = {Configurable software systems allow stakeholders to derive program variants by selecting features. Understanding the correlation between feature selections and performance is important for stakeholders to be able to derive a program variant that meets their requirements. A major challenge in practice is to accurately predict performance based on a small sample of measured variants, especially when features interact. We propose a variability-aware approach to performance prediction via statistical learning. The approach works progressively with random samples, without additional effort to detect feature interactions. Empirical results on six real-world case studies demonstrate an average of 94 \% prediction accuracy based on small random samples. Furthermore, we investigate why the approach works by a comparative analysis of performance distributions. Finally, we compare our approach to an existing technique and guide users to choose one or the other in practice.},
file = {/home/stefan/Zotero/storage/JFWCLWEI/Guo et al. - 2013 - Variability-aware performance prediction A statis.pdf},
isbn = {978-1-4799-0215-6},
language = {en}
}
@article{kolesnikov_relation_2019,
title = {On the Relation of Control-Flow and Performance Feature Interactions: A Case Study},
shorttitle = {On the Relation of Control-Flow and Performance Feature Interactions},
author = {Kolesnikov, Sergiy and Siegmund, Norbert and K{\"a}stner, Christian and Apel, Sven},
year = {2019},
volume = {24},
pages = {2410--2437},
journal = ESE,
organization = {Kluwer Academic Publishers},
number = {4},
doi="10.1007/s10664-019-09705-w"
}
@article{kolesnikov_tradeoffs_2019,
title = {Tradeoffs in Modeling Performance of Highly Configurable Software Systems},
author = {Kolesnikov, Sergiy and Siegmund, Norbert and K{\"a}stner, Christian and Grebhahn, Alexander and Apel, Sven},
year = {2019},
volume = {18},
pages = {2265--2283},
journal = SoSyM,
number = {3},
doi="10.1007/s10270-018-0662-9",
}
@inproceedings{siegmund_views_2015,
title = {Views on {{Internal}} and {{External Validity}} in {{Empirical Software Engineering}}},
booktitle = ICSE,
author = {Siegmund, Janet and Siegmund, Norbert and Apel, Sven},
year = {2015},
month = may,
pages = {9--19},
publisher = IEEE,
language = {en},
doi="10.1109/ICSE.2015.24"
}
@inproceedings{medeiros_comparison_2016,
author = {Medeiros, Fl\'{a}vio and K\"{a}stner, Christian and Ribeiro, M\'{a}rcio and Gheyi, Rohit and Apel, Sven},
title = {A Comparison of 10 Sampling Algorithms for Configurable Systems},
year = {2016},
publisher = ACM,
booktitle = ICSE,
pages = {643–654},
doi="10.1145/2884781.2884793",
}
@inproceedings{oh_finding_2017,
title = {Finding Near-Optimal Configurations in Product Lines by Random Sampling},
booktitle = {Proceedings of the 2017 11th {{Joint Meeting}} on {{Foundations}} of {{Software Engineering}}},
author = {Oh, Jeho and Batory, Don and Myers, Margaret and Siegmund, Norbert},
year = {2017},
month = aug,
pages = {61--71},
publisher = {{Association for Computing Machinery}},
address = {{Paderborn, Germany}},
doi = {10.1145/3106237.3106273},
abstract = {Software Product Lines (SPLs) are highly configurable systems. This raises the challenge to find optimal performing configurations for an anticipated workload. As SPL configuration spaces are huge, it is infeasible to benchmark all configurations to find an optimal one. Prior work focused on building performance models to predict and optimize SPL configurations. Instead, we randomly sample and recursively search a configuration space directly to find near-optimal configurations without constructing a prediction model. Our algorithms are simpler and have higher accuracy and efficiency.},
file = {/home/stefan/Zotero/storage/KBA38B2R/Oh et al. - 2017 - Finding near-optimal configurations in product lin.pdf},
isbn = {978-1-4503-5105-8},
keywords = {finding optimal configurations,searching configuration spaces,software product lines},
series = {{{ESEC}}/{{FSE}} 2017}
}
@inproceedings{daly_industry_2020,
title = {Industry {{Paper}}: {{The Use}} of {{Change Point Detection}} to {{Identify Software Performance Regressions}} in a {{Continuous Integration System}}},
booktitle = ICPE,
author = {Daly, David and Brown, William and Ingo, Henrik and O'Leary, Jim and Bradford, David},
year = {2020},
pages = {67–75},
publisher = ACM,
doi = " 10.1145/3358960.3375791",
}
@inproceedings{sandoval_alcocer_learning_2016,
title = {Learning from {{Source Code History}} to {{Identify Performance Failures}}},
booktitle = ICPE,
author = {Sandoval Alcocer, Juan Pablo and Bergel, Alexandre and Valente, Marco Tulio},
year = {2016},
pages = {37--48},
publisher = ACM,
doi="10.1145/2851553.2851571",
}
@article{alcocer_prioritizing_2020,
title = {Prioritizing Versions for Performance Regression Testing: {{The Pharo}} Case},
author = {Sandoval Alcocer, Juan Pablo and Bergel, Alexandre and Valente, Marco Tulio},
year = {2020},
volume = {191},
pages = {102415},
journal = {{Science of Computer Programming}},
doi="10.1016/j.scico.2020.102415",
}
@inproceedings{huang_performance_2014,
author = {Huang, Peng and Ma, Xiao and Shen, Dongcai and Zhou, Yuanyuan},
title = {Performance Regression Testing Target Prioritization via Performance Risk Analysis},
year = {2014},
publisher = ACM,
booktitle = ICSE,
pages = {60--71},
doi="10.1145/2568225.2568232",
}
@inproceedings{jamshidi_learning_2018,
author = {Jamshidi, Pooyan and Velez, Miguel and K\"{a}stner, Christian and Siegmund, Norbert},
title = {Learning to Sample: Exploiting Similarities across Environments to Learn Performance Models for Configurable Systems},
year = {2018},
publisher = ACM,
booktitle = FSE,
pages = {71–82},
doi="10.1145/3236024.3236074",
}
@inproceedings{jamishidi_transfer_2017,
author = {Jamshidi, Pooyan and Siegmund, Norbert and Velez, Miguel and K\"{a}stner, Christian and Patel, Akshay and Agarwal, Yuvraj},
title = {Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis},
year = {2017},
publisher = IEEE,
booktitle = ASE,
pages = {497–508},
doi="10.1109/ASE.2017.8115661",
}
@inproceedings{jamshidi_transfer_gp_2017,
author = {Jamshidi, Pooyan and Velez, Miguel and K\"{a}stner, Christian and Siegmund, Norbert and Kawthekar, Prasad},
title = {Transfer Learning for Improving Model Predictions in Highly Configurable Software},
year = {2017},
publisher = IEEE,
booktitle = SEAMS,
pages = {31–41},
doi="10.1109/SEAMS.2017.11",
}
@article{white_selecting_2009,
author = {White, Jules and Dougherty, Brian and Schmidt, Douglas C.},
title = {Selecting Highly Optimal Architectural Feature Sets with Filtered Cartesian Flattening},
journal = JSS,
year = {2009},
publisher = {Elsevier Science Inc.},
address = {USA},
volume = {82},
pages = {1268--1284},
doi="10.1016/j.jss.2009.02.011"
}
@article{lasso,
title={Regression shrinkage and selection via the lasso},
author={Tibshirani, Robert},
journal={Journal of the Royal Statistical Society: Series B (Methodological)},
volume={58},
pages={267--288},
year={1996},
publisher={Wiley Online Library},
doi="10.1111/j.2517-6161.1996.tb02080.x",
}
@article{guo_2018_data,
title={Data-efficient performance learning for configurable systems},
author={Guo, Jianmei and Yang, Dingyu and Siegmund, Norbert and Apel, Sven and Sarkar, Atrisha and Valov, Pavel and Czarnecki, Krzysztof and Wasowski, Andrzej and Yu, Huiqun},
journal={Empirical Software Engineering},
volume={23},
number={3},
pages={1826--1867},
year={2018},
publisher={Springer},
doi={10.1007/s10664-017-9573-6},
}
@inproceedings{luo_2019_cova,
author={Linghui Luo and Eric Bodden and Johannes Späth},
booktitle=ASE,
publisher = IEEE,
title={A Qualitative Analysis of Android Taint-Analysis Results},
year={2019},
volume={},
number={},
pages={102-114},
doi = "10.1109/ASE.2019.00020"
}
@InProceedings{lillack_2014_lotrack_ase,
author = {Lillack, Max and Kästner, Christian and Bodden, Eric},
title = {Tracking load-time configuration options},
booktitle = {ASE},
year = {2014},
pages = {445-456},
month = {09},
publisher = IEEE,
doi = {10.1145/2642937.2643001},
journal = ASE,
}
@article{lillack_2018_lotrack_tse,
author = {Max Lillack and Christian Kästner and Eric Bodden},
journal = TSE,
title = {Tracking Load-Time Configuration Options},
year = {2018},
volume = {44},
number = {12},
pages = {1269-1291},
doi = {10.1109/TSE.2017.2756048},
publisher = IEEE,
}
@article{velez_2020_configcrusher_jase,
title={ConfigCrusher: Towards White-Box Performance Analysis for Configurable Systems},
author={Velez, Miguel and Jamshidi, Pooyan and Sattler, Florian and Siegmund, Norbert and Apel, Sven and K{\"a}stner, Christian},
journal=JASE,
pages={1--36},
year={2020},
publisher=Springer,
doi="10.1007/s10515-020-00273-8"
}
@article{liao_2020_using_emse,
title={Using black-box performance models to detect performance regressions under varying workloads: an empirical study},
author={Liao, Lizhi and Chen, Jinfu and Li, Heng and Zeng, Yi and Shang, Weiyi and Guo, Jianmei and Sporea, Catalin and Toma, Andrei and Sajedi, Sarah},
journal=ESE,
pages={1--31},
year={2020},
publisher=Springer,
doi="https://doi.org/10.1007/s10664-020-09866-z"
}
@article{difallah_oltp_2013,
title={{OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases}},
author={Difallah, Djellel Eddine and Pavlo, Andrew and Curino, Carlo and Cudre-Mauroux, Philippe},
journal={Proceedings of the VLDB Endowment},
pages = {277--288},
year={2013},
publisher={VLDB Endowment},
volume = {7},
number = {4},
month = dec,
doi = {10.14778/2732240.2732246},
}
@misc{tpc_c_2013,
title = {TPC-C Client - Java-based implementation of the industry standard benchmark for database performance evaluation},
author = {V. Stankovic and P. T. Popov},
year = {2016},
organization = {University of London},
url = {https://openaccess.city.ac.uk/id/eprint/16037/}
}
@thesis{deorowicz_universal_2003,
address = {Gliwice},
type = {{PhD} dissertation},
title = {Universal lossless data compression algorithms},
url = {http://sun.aei.polsl.pl/~sdeor/pub/deo03.pdf},
abstract = {The disseration concerns universal lossless data compression algorithms such as LZ, PPM, and BWCA methods. A new algorithm based on the Burrows–Wheeler transform is proposed. Its most important features are improved Itoh–Tanaka method for computing BWT, Weighted Frequency Count transform (instead of the MTF), and weighted probability estimation. The performance of the algorithm is evaluated on the Calgary and Silesia corpora.},
school = {Silesian University of Technology},
author = {Deorowicz, Sebastian},
year = {2003},
file = {Deorowicz - 2003 - Universal lossless data compression algorithms.pdf:/home/stefan/Zotero/storage/IRM3NZ2X/Deorowicz - 2003 - Universal lossless data compression algorithms.pdf:application/pdf}
}
@book{witten_calgary_1994,
author = {Witten, Ian H. and Bell, Timothy C. and Moffat, Alistair},
title = {Managing Gigabytes: Compressing and Indexing Documents and Images},
year = {1994},
isbn = {0442018630},
publisher = {John Wiley \& Sons, Inc.},
address = {USA},
edition = {1st}
}
@article{bell_calgary_1989,
author = {Bell, Timothy and Witten, Ian H. and Cleary, John G.},
title = {Modeling for Text Compression},
year = {1989},
issue_date = {Dec. 1989},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {21},
number = {4},
issn = {0360-0300},
url = {https://doi.org/10.1145/76894.76896},
doi = {10.1145/76894.76896},
journal = {ACM Comput. Surv.},
month = dec,