code_analyzer

CI/CD Integration Guide

This guide explains how to integrate Code Analyzer into your CI/CD pipelines for automated code analysis.

GitHub Actions

Code Analyzer provides built-in GitHub Actions workflows that you can use directly in your projects.

Pre-built Workflows

1. Code Analysis Workflow (code_analyzer.yml): Runs code analysis on every push and pull request
2. CI Workflow (ci.yml): Runs tests, linting, and type checking
3. Release Workflow (release.yml): Automatically publishes to PyPI when tags are created
4. Documentation Workflow (docs.yml): Deploys documentation to GitHub Pages

Using the Code Analysis Workflow

To use the built-in code analysis workflow in your project:

1. Copy the workflow file to your repository:

   mkdir -p .github/workflows
   cp .github/workflows/code_analyzer.yml .github/workflows/
   

2. Customize the workflow according to your project needs: ```yaml

   In your .github/workflows/code_analyzer.yml

   • name: Run code analysis run: | code_analyzer analyze . –complexity –export json,html –export-dir ./reports ```

Example Integration

Here’s a complete example of integrating Code Analyzer into your GitHub Actions workflow:

name: Code Analysis

on:
  push:
    branches: [ main, develop ]
  pull_request:
    branches: [ main ]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v5
      with:
        python-version: "3.13"

    - name: Install uv
      uses: astral-sh/setup-uv@v3

    - name: Install code_analyzer
      run: |
        uv pip install code_analyzer

    - name: Run code analysis
      run: |
        code_analyzer analyze . --complexity --export json,html --export-dir ./reports

    - name: Upload analysis reports
      uses: actions/upload-artifact@v4
      with:
        name: code-analysis-report
        path: ./reports/
        retention-days: 30

GitLab CI/CD

For GitLab CI/CD, you can use the provided .gitlab-ci.yml configuration file.

Using the GitLab CI Configuration

1. Copy the configuration file to your repository:

   cp .gitlab-ci.yml ./
   

2. Customize the configuration according to your project needs.

Example GitLab CI Integration

# In your .gitlab-ci.yml
analyze:
  stage: analyze
  image: python:3.13
  script:
    - pip install code_analyzer
    - code_analyzer analyze . --complexity --export json,html --export-dir ./reports
  artifacts:
    paths:
      - reports/
    expire_in: 1 week

Configuration Options

When integrating Code Analyzer into your CI/CD pipeline, you can customize the analysis with various options:

• --complexity: Include complexity analysis
• --export json,html: Export results in multiple formats
• --export-dir ./reports: Specify export directory
• --ignore-patterns: Ignore specific files or directories

Example with custom configuration:

code_analyzer analyze . \
  --complexity \
  --export json,html,csv \
  --export-dir ./reports \
  --ignore-patterns "tests/*" "docs/*"

Failing Builds Based on Analysis Results

You can configure your CI/CD pipeline to fail builds based on analysis results:

# Example: Fail if any file has >1000 lines
code_analyzer analyze . --export json --export-dir ./reports
python -c "
import json
with open('./reports/report.json') as f:
    data = json.load(f)
for file in data['top_files']:
    if file['file_metrics']['lines_of_code'] > 1000:
        print(f'File {file[\"file_metrics\"][\"path\"]} has {file[\"file_metrics\"][\"lines_of_code\"]} lines, exceeding limit')
        exit(1)
"

Best Practices

1. Run on specific events: Only run analysis on important branches or for specific events
2. Archive reports: Always archive analysis reports as artifacts for later review
3. Set appropriate limits: Configure complexity thresholds that match your team’s standards
4. Integrate with code review: Use analysis results to inform code review processes
5. Monitor trends: Track code quality metrics over time to identify improvements or regressions

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