GitHub Productivity Analytics 🚀

Tags: github-analytics • llm-powered • developer-productivity • team-metrics • ai-insights • gemini-api • code-analysis • git-insights • dashboard • plotly • data-visualization • commit-analysis • productivity-tracking • github-api • team-dashboard • developer-insights • github-stats • analytics

Next-generation GitHub organization analytics with AI-powered insights and beautiful interactive dashboards for tracking developer productivity, commit quality, and team performance metrics.

A powerful, AI-enhanced toolkit for analyzing GitHub organization activity and generating comprehensive productivity insights. Features LLM-powered commit analysis, developer performance summaries, comparative metrics, and changelog-style reporting that goes beyond traditional metrics to understand what developers actually build and achieve.

Features

🤖 AI-Powered Analysis

• LLM-Enhanced Commit Analysis: Gemini API integration for intelligent commit assessment
• Business Impact Scoring: AI evaluation of actual business value delivered (0-10 scale)
• Feature Type Classification: Automatic categorization (feature/bugfix/refactoring/etc.)
• Complexity Assessment: AI-driven complexity analysis (low/medium/high/very_high)
• Risk Level Detection: Intelligent risk assessment for commits and changes
• Learning Indicators: AI detection of developer growth patterns and struggles

📊 Advanced Analytics & Metrics

• Multi-Repository Analysis: Analyze all repositories in your GitHub organization (public and private)
• Comparative Developer Performance: Percentile rankings and multi-dimensional comparisons
• Traditional + AI Quality Scoring: Both rule-based and LLM-powered quality assessment
• Team Performance Benchmarking: Advanced comparative metrics across time horizons
• Developer Performance Radar: Multi-dimensional performance visualization
• Changelog-Style Summaries: Semantic understanding of what developers actually built
• Time-based Analysis: Working hours patterns, weekly trends, productivity cycles

🎯 Enhanced Dashboard Experience

• Performance Radar Charts: Multi-dimensional developer comparison visualization
• Business Impact vs Quality Scatter: Advanced performance positioning analysis
• Feature Distribution Analysis: Understanding of work type patterns by developer
• Complexity Trends: Track increasing sophistication of work over time
• Achievement Timelines: Changelog-style view of developer contributions and milestones
• Risk Assessment Views: Visual analysis of code risk patterns
• Comparative Performance Matrix: Heat-map style developer comparison across all metrics

🔧 Smart Features

• Intelligent Caching: LLM analysis results cached to minimize API costs
• Batch Processing: Efficient analysis of large commit volumes
• Author Deduplication: Intelligent mapping of multiple author identities
• Team Filtering: Focus on core team members, exclude bots and external contributors
• Private Repository Support: Full GitHub Enterprise and private org support
• Configurable Scoring: Customize both traditional and AI-powered quality metrics
• Export Capabilities: Enhanced CSV data export with AI insights
• CI/CD Integration: Automated reporting and monitoring with AI summaries

💻 Developer Experience

• Easy Setup: One-command configuration with guided LLM API setup
• Cost-Effective: Intelligent caching and batching to minimize LLM API costs
• Multiple Dashboard Types: Both traditional and AI-enhanced dashboard options
• Extensible: Plugin architecture for custom metrics and AI prompts
• Well Documented: Comprehensive guides including AI configuration
• Open Source: MIT licensed, community-driven development

Dashboard Screenshots

Traditional Dashboard

The standard dashboard (web_dashboard.py) includes:

• Weekly commit activity trends with filtering
• Quality score progression over time
• Lines of code metrics and repository heatmaps
• Working hours analysis and performance comparisons
• Time period filtering (last 7/30/90 days, quarters, etc.)

AI-Enhanced Dashboard

The enhanced dashboard (enhanced_dashboard.py) features:

• Performance Radar Charts: Multi-dimensional developer comparison
• Business Impact vs Quality Analysis: AI-powered positioning of developers
• Feature Distribution Charts: Understanding what type of work each developer does
• Complexity Trend Analysis: Tracking sophistication of work over time
• Achievement Timeline: Changelog-style view of developer milestones
• Risk Assessment Visualization: Visual analysis of code risk patterns
• Developer Summary Reports: AI-generated performance summaries with key achievements

Use Cases

For Engineering Managers

• AI-Powered Performance Reviews: Get comprehensive, data-driven developer assessments
• Business Impact Tracking: Understand which developers deliver the most business value
• Skill Gap Analysis: AI detection of learning patterns and growth opportunities
• Resource Planning: Comparative analytics for sprint planning and task allocation
• Quality Trend Analysis: Monitor code quality improvements with traditional + AI metrics

For Technical Leads

• Mentoring Insights: AI-generated developer summaries with specific growth recommendations
• Code Review Optimization: Risk assessment and complexity analysis for better reviews
• Technical Debt Monitoring: AI analysis of code complexity and maintenance burden
• Knowledge Transfer: Understanding who has expertise in different areas of the codebase
• Architecture Decision Support: Impact analysis of technical changes and initiatives

For Development Teams

• Personal Growth Tracking: AI-powered analysis of your coding evolution and achievements
• Comparative Benchmarking: See how your performance compares within the team context
• Learning Path Optimization: AI recommendations for skill development priorities
• Sprint Retrospective Data: Rich analytics for team retrospectives and process improvement
• Achievement Recognition: Changelog-style summaries of your contributions and impact

For Organizations

• ROI Measurement: Business impact scoring to measure development investment returns
• Talent Assessment: AI-powered evaluation for hiring, promotion, and team formation decisions
• Process Optimization: Data-driven insights for improving development workflows
• Benchmark Establishment: Comparative metrics for organizational performance standards
• Strategic Planning: Long-term trend analysis for technology and team investment decisions

Installation

Option 1: Using Conda (Recommended)

# Clone the repository
git clone https://github.com/sdntsng/gh-productivity.git
cd gh-productivity

# Create and activate conda environment
conda env create -f environment.yml
conda activate gh-productivity

Option 2: Using pip

# Clone the repository
git clone https://github.com/sdntsng/gh-productivity.git
cd gh-productivity

# Install dependencies
pip install -r requirements.txt

Configuration

Quick Setup

1. Run the setup script:

   python setup.py

   This will create config.py from the example and guide you through the setup.

2. Edit config.py with your settings:

   # GitHub API Configuration
   GITHUB_TOKEN = "your_github_token_here"
   ORG_NAME = "your_organization_name"
   
   # LLM Analysis Configuration (Optional but Recommended)
   GEMINI_API_KEY = "your_gemini_api_key_here"  # Get from Google AI Studio
   ENABLE_LLM_ANALYSIS = True  # Enable AI-powered insights
   
   # Core Team Members (customize for your team)
   CORE_TEAM = [
       "developer1",
       "developer2", 
       "developer3"
   ]
   
   # Add author mappings if team members use different names/emails
   AUTHOR_MAPPING = {
       "[email protected]": "developer1",
       "Developer One": "developer1",
   }

3. Test your setup:

   python test_setup.py

   This will verify that everything is configured correctly.

Manual Configuration

If you prefer manual setup:

1. Copy the example configuration file:

   cp config.example.py config.py

2. Generate API Keys:

   GitHub Personal Access Token:

   • Go to GitHub Settings → Developer settings → Personal access tokens
   • Generate a token with these scopes: repo, read:org, read:user
   • For private repositories, ensure your token has appropriate access

   Gemini API Key (Optional but Recommended):

   • Visit Google AI Studio
   • Create a new API key for Gemini Pro
   • This enables AI-powered commit analysis and developer insights

Usage

Step 1: Extract Data

python extract.py

This will:

• Fetch all repositories from your organization
• Analyze commit history and messages
• Generate commit_analysis.csv and developer_productivity.csv

Step 2A: Generate Traditional Dashboard

python web_dashboard.py

Creates productivity_dashboard.html with time-series analysis, filtering, and traditional metrics.

Step 2B: Generate AI-Enhanced Dashboard (Recommended)

python enhanced_dashboard.py

Creates enhanced_dashboard.html with:

• AI-powered commit analysis and business impact scoring
• Comparative performance radar charts
• Developer achievement summaries and changelog views
• Advanced risk assessment and complexity analysis

Note: Requires Gemini API key for full functionality. Falls back to traditional analysis if not configured.

Optional: Static Charts

python dashboard.py

Generates productivity_dashboard.png with static matplotlib charts.

What You Get

CSV Outputs

• commit_analysis.csv: Detailed commit-level data with quality scores and AI insights
• developer_productivity.csv: Aggregated developer statistics and rankings
• llm_analysis_cache.json: Cached AI analysis results to minimize API costs

Traditional Dashboard (productivity_dashboard.html)

• Weekly activity trends with developer filtering
• Commit quality progression over time with multiple time period views
• Lines of code statistics (additions, deletions, net changes)
• Repository activity heatmaps and working hours analysis
• Performance comparison tables with conditional formatting
• Mobile-responsive design with interactive charts

Enhanced AI Dashboard (enhanced_dashboard.html)

• Performance Radar Charts: Multi-dimensional developer comparison across quality, productivity, and impact
• Business Impact Analysis: AI-scored business value positioning of each developer
• Achievement Timelines: Changelog-style visualization of developer milestones and contributions
• Feature Distribution: Understanding of work type patterns (features vs bugs vs refactoring)
• Complexity Trend Analysis: Tracking the sophistication of work over time
• Risk Assessment: Visual analysis of code risk patterns and quality concerns
• Developer Summary Reports: AI-generated performance summaries with specific achievements and growth recommendations

Advanced Configuration

The config.py file offers extensive customization options:

LLM Analysis Configuration

Control AI-powered analysis features:

# Gemini API Configuration
GEMINI_API_KEY = "your_api_key_here"
GEMINI_MODEL = "gemini-pro"
ENABLE_LLM_ANALYSIS = True     # Enable AI-powered insights
LLM_BATCH_SIZE = 10            # Commits per batch (cost optimization)
LLM_ANALYSIS_CACHE_DAYS = 7    # Cache duration to minimize API costs

Quality Scoring

Customize traditional quality scoring:

QUALITY_BASE_SCORE = 5          # Starting score for all commits
QUALITY_MIN_LENGTH = 10         # Minimum characters for good message
QUALITY_GOOD_LENGTH = 50        # Characters for descriptive message bonus
VAGUE_WORDS = ['fix', 'update', 'change']  # Words that reduce quality score
CONVENTIONAL_COMMIT_PATTERN = r'^(feat|fix|docs|style|refactor|test|chore)(\(.+\))?: .+'

Data Collection

Control what data is collected:

INCLUDE_PRIVATE = True      # Include private repositories
EXCLUDE_ARCHIVED = True     # Exclude archived repositories  
INCLUDE_STATS = True        # Include detailed line count statistics
ANALYSIS_DAYS = 180         # Number of days to look back for commits

Team Management

Filter and organize your team data:

CORE_TEAM = ["dev1", "dev2", "dev3"]  # Your core team members

# Author name mapping for deduplication
AUTHOR_MAPPING = {
    'Samyak Gupta': 'samyaksgupta',
    'samyaksgupta': 'samyaksgupta',
    '[email protected]': 'dev'
}

# External contributors to exclude
EXCLUDED_AUTHORS = {
    "github-actions[bot]",
    "dependabot[bot]",
    "external-contractor"
}

Feature Flags

Enable/disable specific analysis features:

ENABLE_LLM_ANALYSIS = True      # AI-powered commit analysis
ENABLE_WEEKLY_TRENDS = True     # Week-on-week trend analysis
ENABLE_WORKING_HOURS = True     # Working hour pattern analysis
ENABLE_REPOSITORY_STATS = True  # Repository-level statistics
ENABLE_QUALITY_ANALYSIS = True  # Commit message quality analysis

Private Repository Access

For organizations with private repositories:

1. Install GitHub CLI:

   # macOS
   brew install gh
   
   # Other platforms: https://cli.github.com/

2. Authenticate with GitHub CLI:

   gh auth login

The tool will automatically use GitHub CLI for private repository access when needed.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

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

License

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

Acknowledgments

• Built with Plotly for interactive visualizations
• Uses pandas for data analysis
• GitHub API integration with fallback to GitHub CLI

Support

If you encounter any issues or have questions:

1. Check the Issues page for existing solutions
2. Create a new issue with detailed information about your problem
3. Include your configuration (without sensitive tokens) and error messages

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Made with care for developer productivity insights