分布式代码审查的AI增强：跨团队PR语义摘要与冲突预警

class PRSummarizer:
    def generate_summary(self, pr: PullRequest) -> Summary:
        """
        生成PR的语义摘要
        """
        summary = Summary()
        
        # 1. 提取意图
        summary.intent = self.llm.extract_intent(
            pr.description,
            pr.commits,
            pr.changed_files
        )
        
        # 2. 分析变更类型
        summary.change_type = self.classify_change(pr.diff)
        # 类型：功能新增、Bug修复、重构、性能优化、安全修复...
        
        # 3. 识别受影响组件
        summary.affected_components = self.analyze_impact(
            pr.changed_files,
            dependency_graph
        )
        
        # 4. 评估兼容性
        summary.compatibility = self.assess_compatibility(
            pr.diff,
            public_apis
        )
        
        # 5. 提取审查重点
        summary.review_focus = self.identify_review_focus(
            pr.diff,
            summary.change_type
        )
        
        # 6. 检查测试覆盖
        summary.test_coverage = self.analyze_test_coverage(
            pr.changed_files,
            pr.test_files
        )
        
        return summary
    
    def identify_review_focus(self, diff: Diff, change_type: str) -> List[Focus]:
        """
        识别需要重点审查的代码区域
        """
        focus_areas = []
        
        # 根据变更类型识别重点
        if change_type == "API_CHANGE":
            focus_areas.append(Focus(
                area="接口定义",
                reason="公共API变更影响广泛",
                check_items=["向后兼容性", "文档同步", "版本控制"]
            ))
        
        if change_type == "SECURITY_FIX":
            focus_areas.append(Focus(
                area="安全逻辑",
                reason="安全修复需特别仔细",
                check_items=["漏洞是否完全修复", "无回归风险", "测试覆盖攻击场景"]
            ))
        
        # AI分析：识别高风险代码模式
        risky_patterns = self.llm.identify_risky_patterns(diff)
        for pattern in risky_patterns:
            focus_areas.append(Focus(
                area=pattern.location,
                reason=pattern.risk_description,
                check_items=pattern.verification_steps
            ))
        
        return focus_areas

class ConflictPredictor:
    def predict_conflicts(self, open_prs: List[PR]) -> ConflictReport:
        """
        预测PR间的潜在冲突
        """
        conflicts = []
        
        # 1. 分析PR间的依赖关系
        for pr1, pr2 in combinations(open_prs, 2):
            # 检查文件冲突
            if self.has_file_overlap(pr1, pr2):
                conflicts.append(FileConflict(pr1, pr2))
            
            # 检查接口冲突
            if self.has_api_conflict(pr1, pr2):
                conflicts.append(APIConflict(
                    pr1, pr2,
                    details=self.analyze_api_conflict(pr1, pr2)
                ))
            
            # 检查语义冲突（AI分析）
            semantic_conflict = self.detect_semantic_conflict(pr1, pr2)
            if semantic_conflict:
                conflicts.append(semantic_conflict)
        
        # 2. 评估冲突严重程度
        for conflict in conflicts:
            conflict.severity = self.assess_severity(conflict)
            conflict.resolution_strategy = self.suggest_resolution(conflict)
        
        return ConflictReport(conflicts=conflicts)
    
    def detect_semantic_conflict(self, pr1: PR, pr2: PR) -> Optional[SemanticConflict]:
        """
        检测两个PR的语义冲突
        """
        # 分析PR1的意图
        intent1 = self.llm.extract_intent(pr1)
        # 分析PR2的意图
        intent2 = self.llm.extract_intent(pr2)
        
        # 检查意图是否矛盾
        if self.are_intents_conflicting(intent1, intent2):
            return SemanticConflict(
                pr1=pr1,
                pr2=pr2,
                conflict_type="INTENT_CONFLICT",
                explanation=self.llm.explain_conflict(intent1, intent2),
                suggestion=self.llm.suggest_reconciliation(intent1, intent2)
            )
        
        # 检查实现是否冲突
        impl_conflict = self.check_implementation_conflict(pr1, pr2)
        if impl_conflict:
            return SemanticConflict(
                pr1=pr1,
                pr2=pr2,
                conflict_type="IMPLEMENTATION_CONFLICT",
                details=impl_conflict
            )
        
        return None

class ReviewerRecommender:
    def recommend_reviewers(self, pr: PR) -> List[ReviewerRecommendation]:
        """
        推荐最佳审查者
        """
        candidates = []
        
        # 1. 代码所有权分析
        code_owners = self.get_code_owners(pr.changed_files)
        for owner in code_owners:
            candidates.append(Candidate(
                user=owner,
                reason="代码所有者",
                score=0.9
            ))
        
        # 2. 历史审查分析
        similar_prs = self.find_similar_prs(pr)
        for similar_pr in similar_prs:
            for reviewer in similar_pr.reviewers:
                candidates.append(Candidate(
                    user=reviewer,
                    reason="审查过类似变更",
                    score=0.8
                ))
        
        # 3. 知识图谱匹配
        domain_experts = self.find_domain_experts(pr)
        for expert in domain_experts:
            candidates.append(Candidate(
                user=expert,
                reason="领域专家",
                score=0.85
            ))
        
        # 4. 负载均衡考虑
        final_recommendations = self.balance_workload(candidates)
        
        return final_recommendations
    
    def find_domain_experts(self, pr: PR) -> List[User]:
        """
        基于知识图谱找到领域专家
        """
        # 提取PR涉及的领域概念
        concepts = self.llm.extract_concepts(pr)
        
        # 在知识图谱中查找专家
        experts = []
        for concept in concepts:
            experts.extend(knowledge_graph.find_experts(concept))
        
        return experts

class MultiTeamReview:
    def coordinate_review(self, pr: PR) -> ReviewPlan:
        """
        协调跨团队审查
        """
        plan = ReviewPlan()
        
        # 分析PR影响的团队
        affected_teams = self.identify_affected_teams(pr)
        
        for team in affected_teams:
            # 确定该团队的审查重点
            focus = self.determine_focus(pr, team)
            
            # 推荐该团队的审查者
            reviewers = self.recommend_team_reviewers(team, focus)
            
            plan.add_team_review(
                team=team,
                reviewers=reviewers,
                focus_areas=focus,
                required_approvals=1  # 每个团队至少1个批准
            )
        
        # 设置审查顺序
        plan.review_order = self.optimize_review_order(affected_teams)
        
        return plan

class ReviewKnowledgeExtractor:
    def extract_knowledge(self, pr: PR) -> List[KnowledgeItem]:
        """
        从Code Review中提取知识
        """
        knowledge_items = []
        
        for comment in pr.comments:
            # 识别有价值的评论
            if self.is_valuable_insight(comment):
                knowledge = self.llm.extract_knowledge(comment)
                
                knowledge_items.append(KnowledgeItem(
                    type=knowledge.type,
                    content=knowledge.content,
                    context={
                        "file": comment.file,
                        "line": comment.line,
                        "pr": pr.number
                    },
                    tags=knowledge.tags
                ))
        
        return knowledge_items
    
    def categorize_knowledge(self, items: List[KnowledgeItem]):
        """
        分类知识并更新知识库
        """
        for item in items:
            if item.type == "BEST_PRACTICE":
                self.knowledge_base.add_best_practice(item)
            elif item.type == "COMMON_MISTAKE":
                self.knowledge_base.add_pattern(
                    pattern=item.content,
                    type="anti-pattern"
                )
            elif item.type == "DESIGN_DECISION":
                self.knowledge_base.add_decision_record(item)

class KnowledgeReuse:
    def suggest_patterns(self, pr: PR) -> List[PatternSuggestion]:
        """
        基于历史知识建议审查模式
        """
        suggestions = []
        
        # 识别PR中的代码模式
        patterns = self.identify_patterns(pr)
        
        for pattern in patterns:
            # 查找历史类似模式
            similar = self.knowledge_base.find_similar_patterns(pattern)
            
            for historical in similar:
                if historical.outcome == "ISSUE_FOUND":
                    suggestions.append(PatternSuggestion(
                        pattern=pattern,
                        historical_issue=historical.issue,
                        suggestion=historical.resolution,
                        confidence=historical.confidence
                    ))
        
        return suggestions
    
    def auto_comment(self, pr: PR) -> List[AutoComment]:
        """
        基于知识库自动生成审查评论
        """
        comments = []
        
        # 检查常见错误
        for anti_pattern in self.knowledge_base.anti_patterns:
            matches = self.find_pattern_in_code(pr, anti_pattern)
            for match in matches:
                comments.append(AutoComment(
                    file=match.file,
                    line=match.line,
                    body=f"⚠️ 检测到潜在问题：{anti_pattern.description}\n\n"
                         f"建议：{anti_pattern.suggestion}\n\n"
                         f"参考：[类似案例]({anti_pattern.reference})"
                ))
        
        return comments