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.

• Set up FastAPI application with lifespan management for background tasks.
• Implement configuration loading from config.json and .env.
• Create abstract BaseReview class with GitHub (GitHubReview) and GitLab (GitLabReview) implementations.
• Add AI client abstraction (AIClient) supporting OpenAI and Ollama.
• Implement code review logic with detailed comments and summaries.
• Add /review API endpoint for reviewing code changes.
• Set up CLI interface for manual testing.
• 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