Gitlights - AI-powered Git analytics for enhanced team performance.

Gitlights AI-powered Git analytics for enhanced team performance.

What is Gitlights?

Gitlights utilizes cutting-edge technology, including AI and NLP algorithms, to provide advanced analytics and insights into software development processes. It analyzes commits and pull requests, offering visualizations of code evolution, including added/deleted lines and overall balance through historical graphs. The platform allows users to assess development team performance using metrics like RSI and EMA, drawing insights from developer comments, reviews, and conversations within pull requests.

Further capabilities include detailed analysis of individual developer skills, identifying strengths, areas for improvement, and contributions towards fixes, refactoring, or reviews. The tool automatically categorizes commits (e.g., Fixes, Refactoring, New Development, Security, Documentation) to monitor the team's investment balance against company goals. It also features a Developers Map to visualize team dynamics and offers benchmarking against industry standards or similar-sized companies, facilitating strategic decision-making and performance optimization.

Features

Insightful Commits and Pull Requests Dashboard: Visualize code evolution, added/deleted lines, balance, and assess performance with RSI/EMA metrics.
Advanced Developer Skills Analysis: Identify individual developer strengths, improvement areas, and contribution types (problem-solving, fixes, refactoring, reviews).
Strategic Investment Balance Monitoring: AI categorizes commits (Fixes, Refactoring, New, Development, Security, Documentation) to track development focus.
Collaborative Developers Map: Distinguish between individual and collective contributors to understand team dynamics.
Benchmarking: Compare team performance against industry standards or similar companies.
Smart Notifications: Receive AI-driven weekly/monthly reports and insights via email or Slack.
AI and NLP Algorithms: Leverages artificial intelligence and natural language processing for enriched data analysis.

Use Cases

Analyzing software development team productivity.
Monitoring code repository activity and trends.
Evaluating individual developer skills and contributions.
Tracking development investment across different task types (features, fixes, refactoring).
Benchmarking team performance against industry peers.
Improving collaboration and understanding team dynamics.
Making data-driven decisions for software development strategy.