code-metrics-tracker 0.1.7

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

What's New in 0.1.6 (May 2025)

• Improved Detection: Fixed --only-on-changes flag to correctly identify meaningful changes
• Better Feedback: Added debug output showing which metrics changed between snapshots
• Developer UX: Clear messages when no changes are detected in snapshots
• JSON Files: Added "_unchanged" suffix to metrics files when no changes are detected

What's New in 0.1.5 (May 2025)

• New Feature: Added --only-on-changes flag to prevent redundant CODE_METRICS.md updates
• GitHub Actions Efficiency: Improved GitHub workflow to avoid empty commits
• Bug Fix: Fixed an issue with Radon CC JSON parsing where string entries caused errors
• Improved Stability: Enhanced parser to handle different formats of Radon output
• Better Error Handling: Added specific type checking to prevent AttributeError

What's New in 0.1.4 (May 2023)

• Improved JSON Parsing: All tools now use structured JSON output for better accuracy
• Accurate Complexity Reporting: Fixed function complexity display in reports
• Enhanced Modularity: New dedicated modules for parsing and snapshot management
• Type Safety: Added TypedDict definitions for all metrics data

See the CHANGELOG.md for full details.

Features

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

Install from PyPI

pip install code-metrics-tracker

Install Required Dependencies

The tool relies on three external tools that need to be installed separately:

1. Install cloc

# macOS
brew install cloc

# Ubuntu/Debian
sudo apt-get install cloc

# Windows
choco install cloc

2. Install Ruff and Radon

These are automatically installed as dependencies when you install code-metrics-tracker, but you can also install them directly:

pip install ruff radon

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.

Commands

Command Overview

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

Detailed Usage Examples

Initialize a Project

The init command sets up your project for code quality tracking by:

• Creating a configuration file (codeqa.json)
• Creating a metrics storage directory
• Adding a CODE_METRICS.md file if it doesn't exist

# Basic initialization with default settings
codeqa init

# After initialization, you can edit codeqa.json to customize
# which directories to include/exclude

Create Metrics Snapshots

The snapshot command analyzes your codebase and:

• Runs code statistics with cloc
• Performs linting checks with Ruff
• Analyzes complexity and maintainability with Radon
• Updates CODE_METRICS.md with the latest metrics
• Stores detailed metrics data as a JSON file

# Create a snapshot with default settings
codeqa snapshot

# Create a snapshot with a custom report title
codeqa snapshot --title "Post-Refactoring Metrics"

# Create a snapshot but only update CODE_METRICS.md if there are meaningful changes
codeqa snapshot --only-on-changes

List Available Snapshots

The list command shows all available snapshots:

# List all snapshots with their dates and filenames
codeqa list

Example output:

Available snapshots:
- May 13, 2025 (metrics_20250513_164444.json)
- April 19, 2025 (metrics_20250419_150845.json)
- April 18, 2025 (metrics_20250418_183327.json)

Compare Snapshots

The compare command allows you to track changes between two snapshots:

# Compare by using snapshot filenames
codeqa compare --first generated/metrics/metrics_20250418_183327.json --second generated/metrics/metrics_20250513_164444.json

# Compare and save the report to a file
codeqa compare --first generated/metrics/metrics_20250418_183327.json --second generated/metrics/metrics_20250513_164444.json --output comparison_report.md

# Compare using indexes from the list command (1-based)
codeqa compare --first 2 --second 1 --output comparison_report.md

The comparison report highlights:

• Changes in lines of code
• Changes in linting issues
• Changes in complex functions
• Changes in maintainability
• Trend analysis with percentage changes

Generate Standalone Reports

The report command generates a standalone report from a specific snapshot:

# Generate a report from a specific snapshot
codeqa report --snapshot generated/metrics/metrics_20250513_164444.json

# Save the report to a specific file
codeqa report --snapshot generated/metrics/metrics_20250513_164444.json --output quality_report.md

# Generate a report using the snapshot index from the list command (1-based)
codeqa report --snapshot 1 --output quality_report.md

The standalone report includes:

• Summary statistics
• Code distribution by language
• Top complex files and functions
• Files with linting issues
• Files with low maintainability

Output Formats

CODE_METRICS.md

The main output file is CODE_METRICS.md, which contains:

• A header section explaining the metrics
• Historical snapshots with dates
• Summary statistics for each snapshot
• Code statistics by language
• Lists of complex files and functions
• Files with linting issues
• Files with low maintainability
• Trend analysis compared to the previous snapshot
• Analysis of critical issues to address

Sample CODE_METRICS.md Excerpt

# Code Quality Metrics

This file tracks code quality metrics over time to help monitor and improve our codebase.

## Metrics Definitions

### Ruff Metrics
- **Issues Count**: Total number of linting issues detected by Ruff
- **Issues by Type**: Distribution of error types (unused imports, undefined names, etc.)

### Radon Complexity Metrics (CC)
- **A**: CC score 1-5 (low complexity)
- **B**: CC score 6-10 (moderate complexity)
- **C**: CC score 11-20 (high complexity)
- **D**: CC score 21-30 (very high complexity)
- **E**: CC score 31-40 (extremely high complexity)
- **F**: CC score 41+ (alarming complexity)

## Historical Snapshots

### May 13, 2025

#### Summary

| Metric | Value | 
|--------|-------|
| Lines of Code | 123,739 |
| Files | 699 |
| Comments | 35,493 |
| Linting Issues | 376 |
| Cyclomatic Complexity | A:751 B:102 C:253 D:8 E:3 F:346 |
| Maintainability Index | A:215 B:1 C:3 |

#### Analysis
- Critical issues to address:
  - 376 linting issues
  - 610 high complexity functions
  - 3 files with low maintainability

Comparison Reports

Comparison reports highlight changes between snapshots:

## Comparison: April 19, 2025 vs May 13, 2025

### Summary

| Metric | April 19, 2025 | May 13, 2025 | Change |
|--------|---------|---------|--------|
| Lines of Code | 26,423 | 123,739 | ↑ 97316 (368.3%) |
| Linting Issues | 296 | 376 | ↑ 80 (27.0%) |
| Complex Functions (C-F) | 474 | 610 | ↑ 136 (28.7%) |
| Low Maintainability Files | 3 | 3 | ↑ 0 (0.0%) |

### Analysis

- Code Size: Increased by 97,316 lines
- Code Quality: Mixed changes
- Most Significant Change: Complex Functions

JSON Data Files

Each snapshot also produces a detailed JSON file containing:

• Complete metrics data
• Timestamp information
• Raw data from all tools (cloc, Ruff, Radon)
• Detailed breakdowns by file and function
• Language statistics

These JSON files can be used for:

• Custom analysis scripts
• Integration with other tools
• Historical data processing
• Visualization dashboards

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 cloc
        run: sudo apt-get install -y cloc
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install code-metrics-tracker
      - name: Generate code quality snapshot
        run: codeqa snapshot --only-on-changes  # Only update if there are meaningful changes
      - 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/*

Development Guide

Local Development Setup

1. Clone the repository:

git clone https://github.com/AgileWorksZA/codeqa.git
cd codeqa

2. Install in development mode:

pip install -e .

3. Install development dependencies:

pip install build twine

Creating a New Release

1. Update version numbers in both files:

   • setup.py - Update the version parameter
   • codeqa/__init__.py - Update the __version__ variable

2. Update the README.md with any new features or changes

3. Build the package:

rm -rf dist/ build/ *.egg-info/
python -m build

4. Test the package locally:

pip install dist/*.whl

Publishing to PyPI

1. Install publishing tools if you haven't already:

pip install twine

2. Create a .pypirc file in your home directory with your PyPI credentials:

[pypi]
username = your_username
password = your_password

3. Upload to PyPI:

python -m twine upload dist/*

4. Alternatively, use a token for authentication:

python -m twine upload --username __token__ --password your-pypi-token dist/*

5. Verify the package is available on PyPI: https://pypi.org/project/code-metrics-tracker/

License

MIT