test-analysis-advanced.yml

name: Advanced Test Analysis

on:
  workflow_run:
    workflows: ["Advanced CI"]
    types: [completed]

jobs:
  analyze:
    runs-on: ubuntu-latest
    if: github.event.workflow_run.conclusion == 'success'

    steps:
    - uses: actions/checkout@v3

    - name: Download test results
      uses: actions/download-artifact@v3
      with:
        name: test-results
        path: test-results

    - name: Download test reports
      uses: actions/download-artifact@v3
      with:
        name: test-reports
        path: test-reports

    - name: Install analysis tools
      run: |
        pip install pandas matplotlib seaborn jinja2

    - name: Generate comprehensive analysis
      run: |
        python -c """
        import json
        import pandas as pd
        import matplotlib.pyplot as plt
        import seaborn as sns
        from datetime import datetime
        import os

        # テスト結果の読み込み
        results = []
        for file in os.listdir('test-results'):
            if file.endswith('.json'):
                with open(f'test-results/{file}') as f:
                    results.append(json.load(f))

        # データフレームの作成
        df = pd.DataFrame(results)

        # データの分析
        analysis = {
            'total_tests': len(df),
            'passed_tests': len(df[df['status'] == 'passed']),
            'failed_tests': len(df[df['status'] == 'failed']),
            'test_coverage': df['coverage'].mean(),
            'execution_time': df['execution_time'].sum(),
            'generation_date': datetime.now().isoformat()
        }

        # グラフの生成
        plt.figure(figsize=(12, 6))
        sns.barplot(x='test_type', y='execution_time', data=df)
        plt.title('Test Execution Time by Type')
        plt.savefig('analysis/execution_time.png')

        # 品質指標の分析
        quality_metrics = {
            'coverage_trend': df['coverage'].rolling(window=5).mean().tolist(),
            'failure_rate': (df['status'] == 'failed').mean(),
            'test_density': len(df) / df['execution_time'].sum()
        }

        # パフォーマンス指標の分析
        performance_metrics = {
            'avg_response_time': df['response_time'].mean(),
            'max_response_time': df['response_time'].max(),
            '95th_percentile': df['response_time'].quantile(0.95)
        }

        # 統計データの保存
        with open('analysis/metrics.json', 'w') as f:
            json.dump({
                'analysis': analysis,
                'quality_metrics': quality_metrics,
                'performance_metrics': performance_metrics
            }, f, indent=2)
        """

    - name: Generate quality report
      run: |
        python -c """
        import json
        from datetime import datetime

        # 品質レポートの生成
        with open('analysis/metrics.json') as f:
            metrics = json.load(f)

        with open('quality-report.md', 'w') as f:
            f.write("# Quality Report\n\n")
            f.write(f"Generated on: {datetime.now().isoformat()}\n\n")
            f.write("## Quality Metrics\n\n")
            for metric, value in metrics['quality_metrics'].items():
                f.write(f"- {metric}: {value}\n")
        """

    - name: Generate performance report
      run: |
        python -c """
        import json
        from datetime import datetime

        # パフォーマンスレポートの生成
        with open('analysis/metrics.json') as f:
            metrics = json.load(f)

        with open('performance-report.md', 'w') as f:
            f.write("# Performance Report\n\n")
            f.write(f"Generated on: {datetime.now().isoformat()}\n\n")
            f.write("## Performance Metrics\n\n")
            for metric, value in metrics['performance_metrics'].items():
                f.write(f"- {metric}: {value}\n")
        """

    - name: Generate trend analysis
      run: |
        python -c """
        import json
        from datetime import datetime

        # トレンド分析の生成
        with open('analysis/metrics.json') as f:
            metrics = json.load(f)

        with open('trend-analysis.md', 'w') as f:
            f.write("# Trend Analysis\n\n")
            f.write(f"Generated on: {datetime.now().isoformat()}\n\n")
            f.write("## Coverage Trend\n\n")
            f.write("![](analysis/coverage_trend.png)\n")
        """

    - name: Create GitHub PR comment
      uses: actions/github-script@v6
      with:
        github-token: ${{ secrets.GITHUB_TOKEN }}
        script: |
          const fs = require('fs');
          const qualityReport = fs.readFileSync('quality-report.md', 'utf8');
          const performanceReport = fs.readFileSync('performance-report.md', 'utf8');
          const trendAnalysis = fs.readFileSync('trend-analysis.md', 'utf8');
          const prNumber = context.payload.pull_request?.number;

          if (prNumber) {
            await github.rest.issues.createComment({
              owner: context.repo.owner,
              repo: context.repo.repo,
              issue_number: prNumber,
              body: `# Test Analysis Summary\n\n${qualityReport}\n\n## Performance Metrics\n\n${performanceReport}\n\n## Trend Analysis\n\n${trendAnalysis}`
            });
          }