AI测试平台

class TestCase:
    """测试用例模型"""
    
    id: str
    title: str
    description: str
    category: str  # functional, performance, security...
    priority: str  # P0, P1, P2, P3
    tags: List[str]
    
    # 输入定义
    input_type: str  # text, image, audio, multimodal
    input_schema: Dict
    
    # 期望输出
    expected_type: str  # exact, range, llm_judge
    expected_output: Any
    acceptance_criteria: List[str]
    
    # 元数据
    owner: str
    created_at: datetime
    updated_at: datetime
    coverage: Dict  # 覆盖的模型/任务/场景

class TestExecutor:
    """分布式测试执行器"""
    
    def __init__(self, config: ExecutorConfig):
        self.config = config
        self.dispatcher = TaskDispatcher()
        self.runner = TestRunner()
    
    def execute(self, suite: TestSuite) -> ExecutionResult:
        """
        执行测试套件
        
        特性:
        - 分布式执行
        - 失败重试
        - 并发控制
        - 资源隔离
        """
        # 任务拆分
        tasks = self.dispatcher.split(suite.cases)
        
        # 资源分配
        workers = self.dispatcher.allocate_workers(len(tasks))
        
        # 执行
        results = []
        for task in tasks:
            result = self.runner.run(task)
            results.append(result)
        
        # 汇总
        return ExecutionResult.aggregate(results)

class ResultCollector:
    """测试结果收集器"""
    
    def collect(self, execution_id: str) -> TestResult:
        """
        收集并结构化测试结果
        
        收集内容:
        1. 执行状态
        2. 输入输出
        3. 性能指标
        4. 错误信息
        5. 环境信息
        """
        return TestResult(
            execution_id=execution_id,
            status=self._get_status(execution_id),
            inputs=self._get_inputs(execution_id),
            outputs=self._get_outputs(execution_id),
            performance=self._get_performance(execution_id),
            errors=self._get_errors(execution_id),
            environment=self._get_environment(execution_id),
        )

class LLMJudge:
    """LLM辅助评判器"""
    
    def judge(self, question: str,
              candidate_answer: str,
              ground_truth: str = None) -> Judgment:
        """
        使用LLM评判答案质量
        
        评判维度:
        1. 相关性: 是否回答问题
        2. 准确性: 事实是否正确
        3. 完整性: 是否覆盖所有要点
        4. 流畅性: 表达是否自然
        5. 安全性: 是否有害内容
        """
        prompt = self._build_judge_prompt(
            question, candidate_answer, ground_truth
        )
        
        # 获取LLM评判
        score = self.model.generate(prompt)
        
        return Judgment(
            score=score.score,
            dimensions=score.dimensions,
            reasoning=score.reasoning,
        )

class QualityDashboard:
    """质量指标看板"""
    
    def build_dashboard(self, time_range: DateRange) -> Dashboard:
        """
        构建质量仪表盘
        
        核心指标:
        1. 测试覆盖率
        2. 通过率趋势
        3. Bug分布
        4. 模型性能对比
        5. 回归风险
        """
        dashboard = Dashboard()
        
        # 测试概览
        dashboard.add_widget('test_overview', {
            'total_cases': self._total_cases(time_range),
            'passed': self._passed_count(time_range),
            'failed': self._failed_count(time_range),
            'pass_rate': self._pass_rate(time_range),
        })
        
        # 通过率趋势
        dashboard.add_widget('pass_rate_trend',
            self._pass_rate_trend(time_range)
        )
        
        # 模型性能对比
        dashboard.add_widget('model_comparison',
            self._model_performance_comparison(time_range)
        )
        
        # 回归风险
        dashboard.add_widget('regression_risk',
            self._regression_analysis(time_range)
        )
        
        return dashboard

class ModelServiceAdapter:
    """模型服务适配器"""
    
    def __init__(self, service_type: str):
        self.adapter = self._get_adapter(service_type)
    
    def _get_adapter(self, service_type: str):
        """获取适配器"""
        adapters = {
            'openai': OpenAIAdapter(),
            'vllm': VLLMAdapter(),
            'triton': TritonAdapter(),
            'tfserving': TFServerAdapter(),
        }
        return adapters.get(service_type)