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https://github.com/sydowma/codereviewbot

This tool automates code reviews for GitLab merge requests and GitHub pull requests using OpenAI's GPT model.
https://github.com/sydowma/codereviewbot

ai codereview

Last synced: 5 months ago
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This tool automates code reviews for GitLab merge requests and GitHub pull requests using OpenAI's GPT model.

Awesome Lists containing this project

README 

          
# Code Review Platform Development Outline



## Project Overview

This project aims to create a web-based code review platform that integrates with GitLab and GitHub, leveraging AI-powered code analysis. The backend will be built with FastAPI, and the frontend (to be added later) will provide a user-friendly interface. The platform will support reviewing pull requests (GitHub) and merge requests (GitLab), offering detailed line-by-line feedback and summaries powered by AI (e.g., OpenAI or Ollama).



### Key Features

- Integration with GitLab and GitHub for fetching and reviewing code changes.

- AI-powered code reviews with detailed comments and summaries.

- RESTFul API backend using FastAPI.

- Support for both CLI and web-based usage.

- Extensible architecture for adding more AI providers or repository platforms.

- User authentication and project management (future enhancement).



### Tech Stack

- **Backend**: Python, FastAPI, GitHub/GitLab APIs, OpenAI/Ollama APIs.

- **Frontend** (Future): React, Axios for API calls, Tailwind CSS or similar.



## Development Roadmap



### Phase 1: Backend Foundation (Current Code Base)

**Goal**: Establish a working backend with core functionality based on the provided code.

- [x] Set up FastAPI application with lifespan management for background tasks.

- [x] Implement configuration loading from `config.json` and `.env`.

- [x] Create abstract `BaseReview` class with GitHub (`GitHubReview`) and GitLab (`GitLabReview`) implementations.

- [x] Add AI client abstraction (`AIClient`) supporting OpenAI and Ollama.

- [x] Implement code review logic with detailed comments and summaries.

- [x] Add `/review` API endpoint for reviewing code changes.

- [x] Set up CLI interface for manual testing.

- [x] Implement background task to periodically check for new GitHub pull requests.



**Next Steps**:

- Test the current code with real GitHub and GitLab repositories.

- Fix any bugs in the review parsing or comment submission logic.

- Add logging to track errors and review progress.



### Phase 2: Backend Enhancements

**Goal**: Improve robustness, scalability, and usability of the backend.

- [ ] **Authentication**: Add API key or OAuth-based authentication for secure access.

  - Use `fastapi.security` for API key management.

  - Integrate GitHub/GitLab OAuth for user login.

- [ ] **Database Integration**: Store review history and user settings.

  - Use SQLite or PostgreSQL with SQLAlchemy.

  - Create tables for users, reviews, and repository configs.

- [ ] **Queue System**: Handle large review requests asynchronously.

  - Integrate `Celery` with Redis or RabbitMQ for task queuing.

  - Move AI review processing to background tasks.

- [ ] **Error Handling**: Improve exception handling and user feedback.

  - Add detailed error responses in API.

  - Log errors to a file or external service (e.g., Sentry).

- [ ] **Rate Limiting**: Prevent abuse of the API.

  - Use `slowapi` or similar to enforce limits.

- [ ] **Configuration API**: Allow runtime updates to AI provider/model via API.



**Deliverables**:

- Secure and scalable backend API.

- Database schema and initial data population.

- Documentation for new endpoints (e.g., `/auth`, `/config`).



### Phase 3: Frontend Development

**Goal**: Build a basic web interface for interacting with the backend.

- [ ] **Setup**: Choose a frontend framework (e.g., React, Vue.js) and set up the project.

- [ ] **Authentication UI**: Login page with GitHub/GitLab OAuth.

- [ ] **Dashboard**: Display list of connected repositories and recent reviews.

- [ ] **Review Page**: Show pull/merge request details with AI comments and summary.

- [ ] **Settings Page**: Configure AI provider, model, and repository settings.

- [ ] **API Integration**: Use Axios or Fetch to connect to FastAPI endpoints.

- [ ] **Styling**: Apply a CSS framework (e.g., Tailwind CSS) for a clean UI.



**Deliverables**:

- Working frontend prototype.

- Basic navigation and styling.

- Integration with backend API endpoints.



### Phase 4: Advanced Features

**Goal**: Add features to enhance user experience and platform capabilities.

- [ ] **Multi-Repository Support**: Allow users to connect multiple GitHub/GitLab repositories.

- [ ] **Custom Prompts**: Enable users to define custom AI review prompts via UI.

- [ ] **Review History**: Show past reviews with filtering and search.

- [ ] **Notifications**: Email or webhook notifications for new reviews.

- [ ] **Team Features**: Support for multiple users per project with roles (e.g., reviewer, admin).

- [ ] **Performance Optimization**: Cache AI responses and optimize diff parsing.



**Deliverables**:

- Enhanced platform functionality.

- User documentation for advanced features.



### Phase 5: Deployment and Testing

**Goal**: Prepare the platform for production use.

- [ ] **Testing**: Write unit and integration tests for backend (e.g., `pytest`).

- [ ] **CI/CD**: Set up GitHub Actions or GitLab CI for automated testing and deployment.

- [ ] **Deployment**: Deploy backend to a cloud provider (e.g., Heroku, AWS, DigitalOcean).

- [ ] **Monitoring**: Add logging and monitoring (e.g., Prometheus, Grafana).

- [ ] **Documentation**: Write full API docs with OpenAPI/Swagger and user guide.



**Deliverables**:

- Production-ready application.

- Comprehensive test suite.

- Deployment scripts and documentation.



## License

[Apache 2.0 License](LICENSE)