code-metrics-tracker 0.1.2

Code quality metrics tracking tool that combines Ruff, Radon, and cloc to generate detailed trends over time

Code Metrics Tracker

A powerful code quality metrics tracking tool for Python projects that helps teams monitor and improve their codebase over time. Code Metrics Tracker leverages three specialized tools to provide comprehensive analysis:

• cloc: Counts lines of code, comments, and blank lines across programming languages
• Ruff: Identifies linting issues, code style violations, and potential bugs with fast performance
• Radon: Analyzes code complexity (cyclomatic complexity) and maintainability

The tracker generates detailed reports focusing on:

• Lines of code statistics: Track code volume and distribution by language
• Linting issues: Detect and monitor code style, potential bugs, and anti-patterns
• Cyclomatic complexity: Identify complex functions and methods that need refactoring
• Maintainability index: Measure how maintainable your code is over time

Results are stored in two complementary formats:

1. JSON snapshots: Detailed metrics data stored in versioned JSON files for programmatic analysis
2. Markdown reports: Human-readable CODE_METRICS.md file that tracks metrics over time with trend indicators

Perfect for teams that want to:

• Track code quality trends over time
• Identify problematic areas in the codebase
• Make data-driven refactoring decisions
• Establish quality standards with measurable metrics
• Integrate metrics tracking into CI/CD pipelines

Installation

pip install code-metrics-tracker

Quick Start

1. Initialize code quality tracking in your project:

codeqa init

2. Create a code quality snapshot:

codeqa snapshot

3. View the generated CODE_METRICS.md file for detailed metrics.

Features

• Track code quality metrics over time
• Generate formatted Markdown reports
• Compare snapshots to identify trends
• Highlight critical issues to address
• Configurable for different project structures

Commands

• codeqa init - Initialize code quality tracking in your project
• codeqa snapshot - Create a new code quality snapshot
• codeqa list - List all available snapshots
• codeqa compare - Compare two snapshots to see trends
• codeqa report - Generate a standalone report from a snapshot

Configuration

The tool uses a codeqa.json configuration file to determine which directories to analyze, focusing only on code maintained by your team.

Example configuration:

{
  "include_paths": ["src", "tests"],
  "exclude_patterns": ["venv", "site-packages", "__pycache__", ".pyc"]
}

GitHub Actions Integration

Add this to your GitHub Actions workflow to automatically track code quality metrics:

name: Code Quality Metrics

on:
  push:
    branches: [ main ]

jobs:
  code-quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install codeqa
      - name: Generate code quality snapshot
        run: codeqa snapshot
      - name: Commit updated CODE_METRICS.md
        uses: stefanzweifel/git-auto-commit-action@v4
        with:
          commit_message: "Update code quality metrics"
          file_pattern: CODE_METRICS.md generated/metrics/*

License

MIT