ML-powered bug prediction system analyzing code commits, complexity metrics and historical bug patterns

Ai Bug Predictor is a production-ready, advanced implementation focusing on real-world use cases and enterprise-grade patterns. This project demonstrates deep expertise in Python 3.10+ · OpenAI · LangChain · FastAPI · ChromaDB.

• ✅ Advanced Architecture — Clean, modular and scalable design patterns
• ✅ Production Ready — Error handling, logging, monitoring and alerting
• ✅ AI-Powered Core — Leverages state-of-the-art LLMs and ML models
• ✅ Comprehensive Tests — Unit, integration and e2e test coverage
• ✅ Docker Support — Containerized for easy deployment
• ✅ CI/CD Ready — GitHub Actions workflows included
• ✅ Well Documented — Inline docs, API reference and examples

Python 3.10+ · OpenAI · LangChain · FastAPI · ChromaDB

git clone https://github.com/gsavla6-hue/ai-bug-predictor.git
cd ai-bug-predictor
pip install -r requirements.txt
cp .env.example .env  # add your API keys
python main.py

ai-bug-predictor/
├── src/                    # Source code
│   ├── core/               # Core business logic
│   ├── api/                # API layer
│   ├── utils/              # Utility functions
│   └── config/             # Configuration
├── tests/                  # Test suites
│   ├── unit/               # Unit tests
│   └── integration/        # Integration tests
├── docs/                   # Documentation
├── docker/                 # Docker configurations
├── .github/workflows/      # CI/CD pipelines
├── requirements.txt        # Dependencies
└── README.md

This project follows a layered architecture with clear separation of concerns:

1. Presentation Layer — API endpoints and user interfaces
2. Business Logic Layer — Core domain logic and AI processing
3. Data Access Layer — Storage, caching and external integrations
4. Infrastructure Layer — Configuration, logging and monitoring

# Run all tests
make test

# Run with coverage
make test-coverage

# Run specific suite
make test-unit

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

MIT License — see LICENSE for details.

Gaurav Savla

• GitHub: @gsavla6-hue
• Email: [email protected]

python scikit-learn code-analysis bug-prediction ml