CI/CD的AI注入点：12个LLM验证卡点设计

def validate_commit_message(message: str, diff: str) -> ValidationResult:
    """
    检查提交信息是否准确描述变更
    """
    issues = []
    
    # 检查类型标签与变更匹配
    if message.startswith("feat:") and "test" in diff.lower():
        issues.append("类型为'feat'但主要是测试代码，考虑用'test:'")
    
    # 检查描述与代码变更一致
    described_files = extract_files_from_message(message)
    actual_files = extract_files_from_diff(diff)
    
    if set(described_files) != set(actual_files):
        issues.append(f"提交信息提到的文件与变更不符")
    
    # 检查是否有关联Issue
    if not re.search(r'#\d+', message) and not is_minor_change(diff):
        issues.append("建议关联相关Issue")
    
    return ValidationResult(passed=len(issues)==0, issues=issues)

def check_intent_alignment(pr_description: str, code_changes: str) -> AlignmentScore:
    """
    验证代码变更是否与PR描述的意图一致
    """
    # 提取PR描述中的意图
    described_intent = llm.extract_intent(pr_description)
    
    # 分析代码变更的实际意图
    actual_intent = llm.analyze_code_intent(code_changes)
    
    # 计算匹配度
    alignment = semantic_similarity(described_intent, actual_intent)
    
    if alignment < 0.7:
        return AlignmentScore(
            score=alignment,
            warning="代码变更与描述意图存在显著偏差",
            details=llm.generate_deviation_report(described_intent, actual_intent)
        )

def semantic_code_review(code: str, context: Context) -> List[Issue]:
    """
    超越语法层面的语义审查
    """
    issues = []
    
    # 检查命名语义
    for var_name in extract_variables(code):
        if is_misleading_name(var_name, code):
            issues.append(Issue(
                type="语义错误",
                message=f"变量'{var_name}'的名称与实际用途不符",
                suggestion=llm.suggest_better_name(var_name, code)
            ))
    
    # 检查设计模式误用
    if detects_pattern_misuse(code):
        issues.append(Issue(
            type="设计缺陷",
            message="检测到设计模式的不当使用",
            explanation=llm.explain_pattern_issue(code)
        ))
    
    # 检查业务逻辑漏洞
    business_issues = llm.analyze_business_logic(code, context.domain)
    issues.extend(business_issues)
    
    return issues

def ai_security_scan(code: str, threat_model: ThreatModel) -> List[Vulnerability]:
    """
    传统SAST发现不了的业务逻辑漏洞
    """
    vulnerabilities = []
    
    # 检查权限绕过
    auth_flow = extract_auth_flow(code)
    if llm.detect_auth_bypass(auth_flow, threat_model):
        vulnerabilities.append(Vulnerability(
            severity="HIGH",
            type="权限绕过",
            description="可能存在越权访问漏洞",
            scenario=llm.generate_attack_scenario(auth_flow)
        ))
    
    # 检查竞争条件
    if has_concurrent_operations(code):
        race_conditions = llm.analyze_race_conditions(code)
        vulnerabilities.extend(race_conditions)
    
    # 检查业务逻辑漏洞（如价格操纵、库存逻辑错误）
    business_vulns = llm.analyze_business_vulnerabilities(code, threat_model)
    vulnerabilities.extend(business_vulns)
    
    return vulnerabilities

def complexity_alert(code: str, historical_data: Data) -> Optional[Alert]:
    """
    检测异常复杂度增长
    """
    current_complexity = calculate_complexity(code)
    baseline_complexity = historical_data.average_complexity
    
    if current_complexity > baseline_complexity * 1.5:
        return Alert(
            level="WARNING",
            message=f"复杂度较基线增长{growth_rate}%",
            details=llm.explain_complexity(code),
            suggestions=llm.suggest_simplification(code)
        )

def assess_dependency_risk(dependencies: List[Dependency]) -> RiskReport:
    """
    AI评估依赖包的风险
    """
    risks = []
    
    for dep in dependencies:
        risk_factors = []
        
        # 检查维护活跃度
        if llm.check_maintainer_activity(dep) == "low":
            risk_factors.append("维护不活跃")
        
        # 检查社区安全声誉
        if llm.check_security_reputation(dep) < 0.5:
            risk_factors.append("安全声誉差")
        
        # 检查与项目技术栈的兼容性
        compatibility = llm.assess_compatibility(dep, project_stack)
        if compatibility < 0.7:
            risk_factors.append(f"技术栈兼容性低({compatibility})")
        
        if risk_factors:
            risks.append(DependencyRisk(
                package=dep.name,
                factors=risk_factors,
                recommendation=llm.suggest_alternative(dep, risk_factors)
            ))
    
    return RiskReport(risks=risks)