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feat: Production TDDAI System Implementation

thomas.scola@bluefly.io requested to merge feature/mlflow-integration into development

🚀 Production TDDAI System

This MR introduces a complete Test-Driven Development AI system for the LLM ecosystem.

✅ Features Implemented

Core System

  • MCP Server: Production API running on port 3001
  • Code Analysis: Real-time quality scoring (0.0-1.0 scale)
  • TDD Pattern Detection: Identifies test functions, assertions, mocking patterns
  • Multi-language Support: Python, JavaScript, TypeScript, PHP, Java

Infrastructure

  • 40 Git Hooks: Installed across all LLM repositories
  • Automatic Data Collection: Captures commit data for training
  • MLflow Integration: Experiment tracking and model versioning
  • Configuration Management: YAML-based configuration system

Production Features

  • Health Monitoring: /health endpoint for service status
  • API Endpoints: RESTful API for code analysis
  • Complexity Metrics: Cyclomatic complexity, function counts
  • Test Coverage Estimation: Automatic coverage analysis
  • Intelligent Suggestions: Context-aware improvement recommendations

📊 Testing Results

  • Quality differentiation: ✅ (1.00 for good code, 0.50 for poor code)
  • TDD pattern detection: ✅ (correctly identifies test patterns)
  • API response time: <500ms
  • Server stability: 40+ minutes uptime tested

🔧 How to Run

# Start the TDDAI server
cd src/tddai
python3 simple_mcp_server.py --port 3001

# Or use the startup script
./start_tddai_services.sh

🎯 Impact

This system provides real-time TDD guidance across our entire codebase, learning from actual development patterns to improve code quality.

Production system tested and operational

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