技术债务的AI识别：债务类型分类与优先级排序

class TechnicalDebt:
    def __init__(self, debt_type, location, severity):
        self.type = debt_type
        self.location = location
        self.severity = severity  # 1-5
        
        # 计算债务成本
        self.interest_rate = self.calculate_interest()  # 持有成本/月
        self.principal = self.calculate_principal()     # 修复成本
        self.impact_scope = self.calculate_impact()     # 影响范围

class ArchitectureDebtDetector:
    def detect(self, codebase):
        debts = []
        
        # 1. 耦合度分析
        coupling_graph = self.build_coupling_graph(codebase)
        high_coupling = self.find_high_coupling(coupling_graph, threshold=0.7)
        
        for pair in high_coupling:
            debts.append({
                'type': 'high_coupling',
                'location': f"{pair.module_a} <-> {pair.module_b}",
                'severity': self.calculate_severity(pair.coupling_score),
                'suggestion': '考虑引入接口层或事件驱动解耦'
            })
        
        # 2. 循环依赖检测
        cycles = self.find_circular_dependencies(codebase)
        for cycle in cycles:
            debts.append({
                'type': 'circular_dependency',
                'location': ' → '.join(cycle.modules),
                'severity': len(cycle.modules),  # 涉及模块越多越严重
                'suggestion': '重构依赖关系，考虑依赖注入'
            })
        
        return debts

class CodeDebtDetector:
    def detect(self, code_file):
        debts = []
        
        # 使用AST分析
        tree = ast.parse(code_file.content)
        
        # 检测长函数
        for node in ast.walk(tree):
            if isinstance(node, ast.FunctionDef):
                lines = node.end_lineno - node.lineno
                if lines > 50:
                    debts.append({
                        'type': 'long_function',
                        'location': f"{code_file.name}:{node.lineno}",
                        'severity': min(lines / 50, 5),
                        'suggestion': f'函数长度{lines}行，建议拆分为小函数'
                    })
        
        # 检测重复代码
        duplicates = self.find_duplicate_code(code_file)
        for dup in duplicates:
            debts.append({
                'type': 'duplicate_code',
                'location': dup.locations,
                'severity': dup.similarity * 5,
                'suggestion': '提取公共函数或类'
            })
        
        return debts

class TestDebtDetector:
    def detect(self, codebase):
        debts = []
        
        for module in codebase.modules:
            # 计算测试覆盖率
            coverage = self.calculate_coverage(module)
            
            if coverage.line_coverage < 0.6:
                debts.append({
                    'type': 'low_coverage',
                    'location': module.name,
                    'severity': (1 - coverage.line_coverage) * 5,
                    'suggestion': f'行覆盖率{coverage.line_coverage:.1%}，建议增加到80%+'
                })
            
            # 检测无测试的关键路径
            untested_critical = self.find_untested_critical_paths(module)
            for path in untested_critical:
                debts.append({
                    'type': 'untested_critical_path',
                    'location': path,
                    'severity': 4,
                    'suggestion': '关键业务路径缺少测试'
                })
        
        return debts

class DocumentationDebtDetector:
    def detect(self, codebase):
        debts = []
        
        for module in codebase.modules:
            # 检查公共API文档
            public_functions = self.get_public_functions(module)
            
            for func in public_functions:
                if not func.has_docstring:
                    debts.append({
                        'type': 'missing_docstring',
                        'location': f"{module.name}:{func.name}",
                        'severity': 2,
                        'suggestion': '为公共函数添加文档字符串'
                    })
                
                # 检查文档是否过时
                if func.has_docstring and self.is_doc_outdated(func):
                    debts.append({
                        'type': 'outdated_documentation',
                        'location': f"{module.name}:{func.name}",
                        'severity': 3,
                        'suggestion': '文档与实现不一致，需要更新'
                    })
        
        return debts

class DebtPrioritizer:
    def prioritize(self, debts, business_context):
        """
        基于多维度对债务进行优先级排序
        """
        scored_debts = []
        
        for debt in debts:
            score = self.calculate_priority_score(debt, business_context)
            scored_debts.append((debt, score))
        
        # 按分数排序
        scored_debts.sort(key=lambda x: x[1], reverse=True)
        
        return scored_debts
    
    def calculate_priority_score(self, debt, context):
        """
        计算债务优先级分数
        """
        # 1. 业务影响（40%）
        business_impact = self.assess_business_impact(debt, context) * 0.4
        
        # 2. 修复成本（20%）
        fix_cost_score = (5 - debt.principal / 100) * 0.2  # 成本越低分数越高
        
        # 3. 持有成本（20%）
        holding_cost_score = debt.interest_rate * 0.2
        
        # 4. 技术风险（20%）
        tech_risk = self.assess_technical_risk(debt) * 0.2
        
        return business_impact + fix_cost_score + holding_cost_score + tech_risk

class DebtPrevention:
    def review_generation(self, generated_code):
        """
        在代码生成时检查潜在债务
        """
        warnings = []
        
        # 检查函数长度
        if self.count_lines(generated_code) > 30:
            warnings.append("生成的函数较长，建议拆分")
        
        # 检查复杂度
        if self.calculate_complexity(generated_code) > 10:
            warnings.append("圈复杂度过高，建议简化")
        
        # 检查重复
        if self.find_similar_code(generated_code):
            warnings.append("检测到相似代码，检查是否可复用")
        
        return warnings