AI 驱动的 CI/CD：代码交付全流程智能化实战指南

# 安装并启用 AI commit 生成
npm install -g @aif/cli
git add .

# AI 分析 diff 并生成 commit message
aif commit

# 输出示例：
# feat(billing): add retry logic for payment webhook
#
# - Implements exponential backoff for Stripe webhook failures
# - Adds idempotency key to prevent duplicate charges
# - BREAKING: payment_service interface signature changed
#
# Co-authored-by: AI <ai@yourcompany.com>

# Claude Code 自动生成测试示例
# 输入：一段新的支付处理函数
async def process_refund(order_id: str, amount: Decimal, reason: str) -> RefundResult:
    """Process a refund for a given order."""
    order = await OrderRepository.get(order_id)
    if order.status != OrderStatus.PAID:
        raise InvalidOrderStatusError(order.status)
    if amount > order.paid_amount:
        raise RefundAmountError(amount, order.paid_amount)
    # ... 实际退款逻辑

# .github/workflows/ai-test-generation.yml
name: AI Test Generation
on:
  pull_request:
    paths:
      - 'src/**/*.py'

jobs:
  generate-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Generate unit tests
        uses: anthropic/claude-code-action/test-generator@v2
        with:
          model: claude-opus-4-5
          target_paths: src/billing/
          output_dir: tests/ai_generated/
          coverage_target: 80%

      - name: Run AI-generated tests
        run: pytest tests/ai_generated/ -v --tb=short

      - name: Create PR with tests
        uses: peter-evans/create-pull-request@v6
        with:
          title: "AI-generated tests for billing module"
          branch: ai-tests/billing-$(date +%s)

# AI 增强的 API 测试生成（基于录制的流量）
# .api-tests/ai-enhanced-scenarios.yaml
scenarios:
  - name: "Order refund edge cases (AI-generated)"
    request:
      method: POST
      path: /api/v1/orders/{order_id}/refund
    variables:
      order_id:
        generative: true  # AI 将生成多种 order_id 类型
        types:
          - paid_order_id
          - refunded_order_id
          - cancelled_order_id
          - non_existent_order_id
    assert:
      - status: 200
        when:
          variables.order_id.type: paid_order_id
      - status: 400
        when:
          variables.order_id.type: refunded_order_id
      - status: 404
        when:
          variables.order_id.type: non_existent_order_id

# .coderabbit.yaml 配置文件
reviews:
  profile: default
  high(summary):
    # 变更超过这些行数，自动生成详细 summary
    lines: 100
  collapseWalkthrough: false
  sections:
    - rationale  # 必须有决策理由
    - test     # 必须有测试覆盖说明
    -北部
      - security  # 安全变更必须有说明
      - breaking  # 破坏性变更必须讨论
  requestChangesWorkflow:
    # PR 作者可以与 AI 对话来修改审查意见
    enabled: true
  manualFilesReply: true

chat:
  # 支持 AI 之间的代码讨论
  autoTitle: passive
  ren收敛: false
  noCode: true

# 简化的 Change Risk Score 计算逻辑
def calculate_change_risk(commit_diff, file_analysis):
    risk_factors = {
        "critical_paths": file_analysis.count_critical_paths(),  # 核心路径变更
        "database_migrations": commit_diff.has_db_migration(),
        "api_contract_changes": commit_diff.breaks_api_compatibility(),
        "security_sensitive": file_analysis.touches_auth_or_payments(),
        "test_coverage_delta": commit_diff.coverage_impact(),
        "code_churn": commit_diff.recent_churn_in_changed_files(),
    }
    
    # 权重模型（可训练调优）
    weights = {
        "critical_paths": 0.3,
        "database_migrations": 0.25,
        "api_contract_changes": 0.2,
        "security_sensitive": 0.15,
        "test_coverage_delta": 0.05,
        "code_churn": 0.05,
    }
    
    score = sum(risk_factors[k] * weights[k] for k in risk_factors)
    return score  # 0.0 (低风险) → 1.0 (高风险)

# ai-canary.yaml
deployment:
  strategy: ai-canary
  
  metrics:
    primary:
      - error_rate        # 目标: < 0.5%
      - p95_latency_ms    # 目标: < 200ms
      - success_rate      # 目标: > 99.5%
    business:
      - checkout_completion_rate  # 购物车完成率
      - api_success_rate          # API 成功率
  
  canary:
    initial_weight: 5
    min_weight: 1
    max_weight: 50
    
  analysis:
    interval_seconds: 60
    warmup_seconds: 300        # 新版本预热期（不计入分析）
    decision_window: 600       # 观察窗口
    
  auto_rollback:
    enabled: true
    error_rate_threshold: 2.0  # 错误率超过 2% 立即回滚
    p95_latency_threshold: 500

name: AI-Augmented CI/CD Pipeline

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

env:
  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
  ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

jobs:
  # ============ 第一关：AI 代码质量检查 ============
  ai-code-quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: AI Commit Message Validation
        if: github.event_name == 'push'
        run: |
          aif validate-commit --commit ${{ github.sha }}
          # 验证 commit message 规范性，不合格则失败构建
      
      - name: AI Real-time Analysis
        uses: anthropic/claude-code-action/static-analyzer@v2
        with:
          paths: src/
          rules:
            - security
            - performance  
            - correctness
          fail_on: high

  # ============ 第二关：AI 测试生成与执行 ============
  ai-test-generation:
    runs-on: ubuntu-latest
    needs: ai-code-quality
    steps:
      - uses: actions/checkout@v4
      
      - name: Generate AI Tests
        run: |
          claude-code --test-gen \
            --target src/billing/ \
            --output tests/ai_generated/ \
            --coverage-target 80
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
      
      - name: Run Tests
        run: |
          pytest tests/ai_generated/ -v --cov=src --cov-report=xml
      
      - name: Upload Coverage
        uses: codecov/codecov-action@v4

  # ============ 第三关：AI Code Review ============
  ai-code-review:
    runs-on: ubuntu-latest
    needs: ai-test-generation
    if: github.event_name == 'pull_request'
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      
      - name: Run CodeRabbit Review
        uses: coderabbitai/github-action@v3
        env:
          REVIEW_TOKEN: ${{ secrets.REVIEW_TOKEN }}
        with:
          repo_token: ${{ secrets.GITHUB_TOKEN }}
          language: zh-CN
          profile: default
          viewed_files: '*'
      
      - name: Run Claude Code Security Scan
        uses: anthropic/claude-code-action/security@v2
        with:
          scan_paths: src/
          severity_threshold: medium
          github_token: ${{ secrets.GITHUB_TOKEN }}

  # ============ 第四关：Change Risk 评估 ============
  change-risk-assessment:
    runs-on: ubuntu-latest
    needs: ai-code-review
    if: github.event == 'pull_request.closed'
    steps:
      - uses: actions/checkout@v4
      
      - name: Calculate Change Risk Score
        id: risk
        uses: your-org/change-risk-evaluator@v3
        with:
          base_branch: main
          head_branch: ${{ github.head_ref }}
          token: ${{ secrets.GITHUB_TOKEN }}
          # 输出：risk_score (0.0-1.0)
      
      - name: Decision Gate
        run: |
          SCORE=${{ steps.risk.outputs.risk_score }}
          if (( $(echo "$SCORE > 0.5" | bc -l) )); then
            echo "HIGH RISK: Requiring manual approval"
            # 通过 GitHub environment protection rules 控制审批流程
          elif (( $(echo "$SCORE > 0.2" | bc -l) )); then
            echo "MEDIUM RISK: Canary deployment will be used"
            # 设置 canary 环境变量
          else
            echo "LOW RISK: Fast-track deployment"
          fi

  # ============ 第五关：AI 监控部署（仅 main 分支）===========
  ai-canary-deploy:
    runs-on: ubuntu-latest
    needs: change-risk-assessment
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      
      - name: AI Canary Deployment
        uses: ai-canary/action@v2
        with:
          app_name: my-service
          image_tag: ghcr.io/myorg/myservice:${{ github.sha }}
          strategy: ai-canary
          metrics_config: .ai-canary.yaml
          
      - name: AI Observability Setup
        run: |
          # 部署后自动配置 AI 监控
          datadog-agent config set AI_OBSERVABILITY enabled
          datadog-agent config set BASELINE_WINDOW 7d
          datadog-agent config set ALERT_THRESHOLD p95_latency:500ms

# 只在以下情况强制要求处理：
feedback_rules:
  - level: require_resolution
    conditions:
      - severity: critical
      - severity: high AND confidence: > 0.8
      - category: security
      - category: breaking_change
  
  # 以下情况仅作为建议，不阻塞合并：
  - level: suggestion
    conditions:
      - severity: medium
      - category: style
      - category: performance AND impact: low