AI Code Review Assistant

An intelligent code review system powered by fine-tuned language models, designed for engineering teams to automate code quality checks, detect security issues, and track metrics over time.

🎯 Key Features

• AI-Powered Code Analysis: Uses fine-tuned LLMs to understand code context across multiple files
• Architectural Improvements: Suggests refactoring and architectural enhancements
• Security Analysis: Flags potential security vulnerabilities and compliance issues
• VCS Integration: Seamless integration with GitHub, GitLab, and Bitbucket
• Webhook Support: Real-time PR/MR analysis with webhook integration
• Team Dashboard: Comprehensive metrics tracking for engineering teams
• Model Fine-Tuning: Train custom models on your codebase and review data
• Context-Aware: Understands cross-file dependencies and relationships

🏗️ Architecture

AI Code Review Assistant
├── Backend API (FastAPI)
│   ├── Webhook handlers
│   ├── Code analysis engine
│   └── Database & caching
├── ML Service (PyTorch)
│   ├── Model fine-tuning pipeline
│   ├── Inference service
│   └── Feature extraction
├── VCS Integration
│   ├── GitHub connector
│   ├── GitLab connector
│   └── Bitbucket connector
├── Dashboard (React/TypeScript)
│   ├── Metrics visualization
│   ├── Review history
│   └── Team analytics
└── Documentation & DevOps
    ├── Docker setup
    └── Deployment configs

📋 Project Structure

.
├── backend/                    # FastAPI backend server
│   ├── app.py
│   ├── requirements.txt
│   ├── config.py
│   ├── models/
│   ├── routes/
│   ├── services/
│   └── utils/
├── ml-service/                 # ML model service
│   ├── train.py
│   ├── inference.py
│   ├── requirements.txt
│   ├── models/
│   ├── datasets/
│   ├── config/
│   └── utils/
├── vcs-integration/            # Version control integrations
│   ├── github_connector.py
│   ├── gitlab_connector.py
│   ├── bitbucket_connector.py
│   ├── base_connector.py
│   ├── requirements.txt
│   └── utils/
├── dashboard/                  # React frontend
│   ├── public/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   └── App.tsx
│   ├── package.json
│   └── tsconfig.json
├── docs/                       # Documentation
│   ├── API.md
│   ├── SETUP.md
│   ├── ARCHITECTURE.md
│   └── CONTRIBUTING.md
├── docker-compose.yml
├── Dockerfile
├── .env.example
└── README.md

🚀 Quick Start

Prerequisites

• Python 3.10+
• Node.js 16+
• Docker & Docker Compose
• PostgreSQL 13+

Installation

1. Clone the repository

   cd /Users/srivardhanreddygutta/Projects/AI-code-review-assistant

2. Backend Setup

   cd backend
   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. ML Service Setup

   cd ml-service
   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

4. Dashboard Setup

   cd dashboard
   npm install
   npm run dev

5. Environment Configuration

   cp .env.example .env
   # Edit .env with your configuration

🔧 Configuration

Create a .env file with:

# Backend
FLASK_ENV=development
API_HOST=0.0.0.0
API_PORT=8000

# Database
DATABASE_URL=postgresql://user:password@localhost/code_review_db

# ML Service
ML_MODEL_PATH=./models/code-review-model
BATCH_SIZE=32

# GitHub Integration
GITHUB_TOKEN=your_token
GITHUB_WEBHOOK_SECRET=your_secret

# GitLab Integration
GITLAB_TOKEN=your_token
GITLAB_WEBHOOK_SECRET=your_secret

# Cache
REDIS_URL=redis://localhost:6379

# Security
JWT_SECRET=your_secret_key

🔑 Key Components

Backend API

• RESTful API for receiving PR/MR data
• Webhook handlers for real-time analysis
• Code analysis orchestration
• Results storage and retrieval

ML Service

• LLM fine-tuning pipeline (CodeBERT, CodeT5, or similar)
• Batch and real-time inference
• Model versioning and A/B testing
• Feature extraction from source code

VCS Integration

• OAuth-based authentication
• Pull request/Merge request parsing
• Diff analysis and context gathering
• Review comment posting

Dashboard

• Real-time metrics visualization
• Code quality trends
• Security vulnerability tracking
• Team productivity analytics

🔍 Core Capabilities

1. Security Issue Detection

• SQL injection patterns
• Credential exposure
• Unsafe cryptography usage
• API key exposure
• XSS vulnerabilities

2. Architectural Improvements

• Circular dependency detection
• Code duplication identification
• Design pattern suggestions
• Performance anti-patterns

3. Code Quality Metrics

• Complexity analysis
• Coverage trends
• Test coverage recommendations
• Documentation gaps

4. Context Understanding

• Cross-file dependency analysis
• Function signature tracking
• Variable flow analysis
• Import resolution

📚 Documentation

• API Documentation
• Setup Guide
• Architecture Guide
• Contributing Guidelines

🤖 Model Fine-Tuning

The project supports fine-tuning models on:

• Your internal code review datasets
• Security vulnerability databases
• Architectural pattern examples
• Your team's coding standards

cd ml-service
python train.py --config configs/finetuning_config.yaml

🐳 Docker Deployment

docker-compose up -d

🧪 Testing

# Backend tests
cd backend && pytest tests/

# ML Service tests
cd ../ml-service && pytest tests/

# Dashboard tests
cd ../dashboard && npm test

📈 Roadmap

• Advanced context aggregation (AST analysis)
• Multi-language support (Java, Go, Rust, etc.)
• IDE integrations (VSCode, IntelliJ)
• Self-hosted model options
• Custom rule engine
• Automated fixes with ML
• Team-specific model adaptation

🤝 Contributing

See CONTRIBUTING.md for guidelines.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Support

For issues, questions, or suggestions, please open an issue on GitHub.

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Built with ❤️ for engineering teams who care about code quality