github-actions-workflow.yml

# GitHub Actions Workflow for AI-Powered DevOps Workshop
# This workflow demonstrates CI/CD with GitHub Copilot and AI tools integration

name: AI-Powered DevOps Workflow

on:
  push:
    branches: [ main, develop ]
    paths:
      - 'src/**'
      - 'tests/**'
      - '.github/workflows/**'
  pull_request:
    branches: [ main ]
  workflow_dispatch:

env:
  AZURE_WEBAPP_NAME: aidevops-workshop-app
  AZURE_WEBAPP_PACKAGE_PATH: '.'
  DOTNET_VERSION: '8.0.x'
  PYTHON_VERSION: '3.11'

jobs:
  build-and-test:
    name: Build and Test with AI Assistance
    runs-on: ubuntu-latest
    
    steps:
    - name: Checkout code
      uses: actions/checkout@v4
      with:
        fetch-depth: 0  # Full history for better AI analysis
    
    - name: Setup .NET
      uses: actions/setup-dotnet@v4
      with:
        dotnet-version: ${{ env.DOTNET_VERSION }}
    
    - name: Setup Python
      uses: actions/setup-python@v4
      with:
        python-version: ${{ env.PYTHON_VERSION }}
    
    # GitHub Copilot integration for code analysis
    - name: AI Code Analysis with Copilot
      run: |
        echo "Running AI-powered code analysis..."
        # This step would integrate with GitHub Copilot CLI or API
        # for automated code review and suggestions
        echo "✓ AI code analysis completed"
    
    # Cache dependencies for faster builds
    - name: Cache dependencies
      uses: actions/cache@v3
      with:
        path: ~/.nuget/packages
        key: ${{ runner.os }}-nuget-${{ hashFiles('**/packages.lock.json') }}
        restore-keys: |
          ${{ runner.os }}-nuget-
    
    - name: Restore dependencies
      run: dotnet restore
    
    - name: Build application
      run: dotnet build --no-restore --configuration Release
    
    # AI-powered testing with intelligent test selection
    - name: Run tests with AI optimization
      run: |
        echo "Running AI-optimized test suite..."
        dotnet test --no-build --configuration Release --verbosity normal \
          --collect:"XPlat Code Coverage" --logger trx --results-directory coverage
        echo "✓ AI-optimized tests completed"
    
    # Publish test results
    - name: Publish test results
      uses: dorny/test-reporter@v1
      if: success() || failure()
      with:
        name: Test Results
        path: coverage/*.trx
        reporter: dotnet-trx
    
    # AI-powered security scanning
    - name: AI Security Scan
      uses: github/super-linter@v4
      env:
        DEFAULT_BRANCH: main
        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        VALIDATE_ALL_CODEBASE: false
        VALIDATE_CSHARP: true
        VALIDATE_PYTHON: true
        VALIDATE_JSON: true
        VALIDATE_YAML: true
    
    # CodeQL Analysis with AI enhancement
    - name: Initialize CodeQL
      uses: github/codeql-action/init@v3
      with:
        languages: csharp, python
        queries: security-and-quality
    
    - name: Perform CodeQL Analysis
      uses: github/codeql-action/analyze@v3
    
    # AI-powered dependency vulnerability scanning
    - name: AI Dependency Scan
      run: |
        echo "Running AI-powered dependency vulnerability scan..."
        dotnet list package --vulnerable --include-transitive
        pip-audit --format=json --output=vulnerabilities.json || true
        echo "✓ AI dependency scan completed"
    
    # Build artifacts
    - name: Publish application
      run: dotnet publish -c Release -o ./publish
    
    - name: Upload artifacts
      uses: actions/upload-artifact@v4
      with:
        name: webapp-artifacts
        path: ./publish
        retention-days: 30

  ai-code-review:
    name: AI-Powered Code Review
    runs-on: ubuntu-latest
    if: github.event_name == 'pull_request'
    
    steps:
    - name: Checkout code
      uses: actions/checkout@v4
      with:
        fetch-depth: 0
    
    # Simulated AI code review (replace with actual AI service)
    - name: AI Code Review Analysis
      run: |
        echo "🤖 Running AI-powered code review..."
        
        # Get changed files
        git diff --name-only origin/main...HEAD > changed_files.txt
        
        echo "📁 Files changed in this PR:"
        cat changed_files.txt
        
        echo ""
        echo "🔍 AI Analysis Results:"
        echo "✓ Code style: Excellent"
        echo "✓ Security: No vulnerabilities detected"
        echo "✓ Performance: Optimizations suggested"
        echo "✓ Maintainability: Good structure"
        
        # In a real implementation, this would call an AI service
        # to analyze the code changes and provide feedback
    
    # Comment on PR with AI insights (requires additional setup)
    - name: Comment PR with AI Insights
      if: github.event_name == 'pull_request'
      run: |
        echo "AI code review completed. Check workflow logs for details."
        # This would post AI-generated comments to the PR

  deploy-to-azure:
    name: Deploy to Azure with AI Monitoring
    needs: build-and-test
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main' && github.event_name == 'push'
    environment: production
    
    steps:
    - name: Download artifacts
      uses: actions/download-artifact@v4
      with:
        name: webapp-artifacts
        path: ./publish
    
    # Azure login
    - name: Azure Login
      uses: azure/login@v1
      with:
        creds: ${{ secrets.AZURE_CREDENTIALS }}
    
    # AI-powered pre-deployment validation
    - name: AI Pre-deployment Validation
      run: |
        echo "🤖 Running AI-powered pre-deployment validation..."
        
        # Simulate AI analysis of deployment readiness
        echo "✓ Code quality metrics: Passed"
        echo "✓ Security scan: Passed"
        echo "✓ Performance prediction: Good"
        echo "✓ Rollback plan: Ready"
        
        # In production, this would integrate with AI services
        # to analyze deployment risk and readiness
    
    # Deploy to Azure App Service
    - name: Deploy to Azure Web App
      uses: azure/webapps-deploy@v2
      with:
        app-name: ${{ env.AZURE_WEBAPP_NAME }}
        package: ./publish
        slot-name: staging
    
    # AI-powered deployment validation
    - name: AI Deployment Validation
      run: |
        echo "🤖 Running AI-powered deployment validation..."
        
        # Health check
        sleep 30  # Wait for deployment to stabilize
        
        # Simulate AI-powered validation
        echo "✓ Application health: Healthy"
        echo "✓ Response time: Optimal"
        echo "✓ Error rate: Within acceptable limits"
        echo "✓ Resource utilization: Normal"
        
        # In production, this would call actual health endpoints
        # and AI services for comprehensive validation
    
    # Swap to production slot
    - name: Swap to Production
      run: |
        az webapp deployment slot swap \
          --resource-group aidevops-workshop-rg \
          --name ${{ env.AZURE_WEBAPP_NAME }} \
          --slot staging \
          --target-slot production
    
    # Setup AI monitoring
    - name: Configure AI Monitoring
      run: |
        echo "🤖 Configuring AI-powered monitoring..."
        
        # Configure Application Insights
        az webapp config appsettings set \
          --resource-group aidevops-workshop-rg \
          --name ${{ env.AZURE_WEBAPP_NAME }} \
          --settings APPLICATIONINSIGHTS_CONNECTION_STRING="${{ secrets.APP_INSIGHTS_CONNECTION_STRING }}"
        
        echo "✓ AI monitoring configured successfully"
    
    # Post-deployment AI analysis
    - name: AI Post-deployment Analysis
      run: |
        echo "🤖 Running AI post-deployment analysis..."
        
        # Simulate real-time AI monitoring setup
        echo "✓ Performance baselines established"
        echo "✓ Anomaly detection enabled"
        echo "✓ Predictive scaling configured"
        echo "✓ User experience monitoring active"
        
        echo "🚀 Deployment completed successfully with AI monitoring!"

  ai-monitoring:
    name: AI-Powered Continuous Monitoring
    needs: deploy-to-azure
    runs-on: ubuntu-latest
    if: always()
    
    steps:
    - name: Setup AI Monitoring Dashboard
      run: |
        echo "🤖 Setting up AI-powered monitoring dashboard..."
        
        # This would integrate with Azure Monitor, Application Insights,
        # and AI services for intelligent monitoring
        echo "✓ Custom metrics configured"
        echo "✓ AI anomaly detection enabled"
        echo "✓ Predictive alerts set up"
        echo "✓ Performance optimization recommendations enabled"
        
        echo "📊 AI monitoring dashboard: https://portal.azure.com/..."
    
    - name: AI Performance Optimization
      run: |
        echo "🤖 Running AI performance optimization analysis..."
        
        # Simulate AI-driven performance recommendations
        echo "💡 AI Recommendations:"
        echo "  • Consider enabling CDN for static assets"
        echo "  • Database query optimization opportunity detected"
        echo "  • Memory usage pattern suggests scaling adjustment"
        echo "  • API response time can be improved with caching"
        
        echo "✓ AI optimization analysis completed"

  cleanup-on-failure:
    name: AI-Assisted Cleanup on Failure
    runs-on: ubuntu-latest
    needs: [build-and-test, deploy-to-azure]
    if: failure()
    
    steps:
    - name: AI Failure Analysis
      run: |
        echo "🤖 Running AI failure analysis..."
        
        # Simulate AI-powered failure analysis
        echo "🔍 AI Failure Analysis Results:"
        echo "  • Root cause: [AI would identify specific issue]"
        echo "  • Impact assessment: [AI would evaluate impact]"
        echo "  • Recommended actions: [AI would suggest fixes]"
        echo "  • Prevention strategies: [AI would recommend improvements]"
    
    - name: Automated Rollback (if needed)
      run: |
        echo "🤖 AI-assisted rollback initiated..."
        
        # In a real scenario, AI would determine if rollback is needed
        # and execute it automatically if safe to do so
        echo "✓ AI-assisted rollback completed"