Welcome to the Automated Code Review Platform repository! This project is designed to simplify and enhance the code review process by leveraging AI to analyze code, detect potential bugs, suggest optimizations, and ensure adherence to coding standards.
This platform provides developers with an intuitive interface for uploading code snippets, receiving actionable insights, and tracking performance metrics through a developer-friendly dashboard.
- Bug Detection: Automatically identifies potential bugs in the code.
- Optimization Suggestions: Provides recommendations to enhance code efficiency and performance.
- Coding Standards Check: Ensures the code adheres to best practices and industry standards.
- Dashboard: Stores past reviews, displays performance metrics, and tracks code quality over time.
- Multi-Language Support: Capable of reviewing code written in various programming languages.
- AI-Powered Analysis: Utilizes large language models (LLMs) to deliver accurate and context-aware reviews.
- Assisting developers in writing cleaner and more efficient code.
- Reducing the time spent on manual code reviews.
- Providing insights into code quality and project performance.
- Helping teams maintain coding standards across projects.
- Gemini API: Core source used for code analysis.
- React: Frontend framework for building the user interface.
- Clone the repository:
git clone https://github.com/TinsaeTadesse17/Automated-Code-Review-Ai-Platform.git cd Automated-Code-Review-Ai-Platform - Install dependencies:
npm install
- Set up your environment variables:
- Add your Gemini API key and other required keys to a .env file
GEMINI_API_KEY=your-api-key
- Start the development server:
npm start
- User Uploads Code: Developers upload code snippets through the web interface.
- AI Analysis: The backend processes the code using AI models to:
- Identify potential bugs.
- Suggest optimizations.
- Verify adherence to coding standards.
- Custom Insights: The system adds actionable feedback and recommendations.
- Dashboard: Past reviews and performance metrics are stored and displayed for easy access.
- Output: Developers receive a detailed and professional code review.
Contributions are welcome! Please follow these steps:
- Fork this repository.
- Create a new branch:
git checkout -b feature-name
3.Make your changes and commit them:
git push origin feature-name 4.Open a pull request.
This project is licensed under the MIT License.
If you have any questions or suggestions, feel free to reach out!
- Email: [email protected]
- LinkedIn: www.linkedin.com/in/tinsae-tadesse-anteneh