HumanEval

from typing import List, Dict, Any, Optional
from dataclasses import dataclass
import ast
import sys
import io
import contextlib

@dataclass
class HumanEvalProblem:
    """HumanEval问题数据结构"""
    task_id: str
    prompt: str
    canonical_solution: str
    test: str
    entry_point: str
    description: Optional[str] = None

class HumanEvalDataset:
    """HumanEval数据集管理"""
    
    def __init__(self, data_path: str = "human-eval/data/HumanEval.jsonl"):
        self.problems = self.load_problems(data_path)
        self.analyze_dataset()
    
    def load_problems(self, data_path: str) -> Dict[str, HumanEvalProblem]:
        """加载HumanEval问题集"""
        import json
        
        problems = {}
        try:
            with open(data_path, 'r') as f:
                for line in f:
                    problem_data = json.loads(line.strip())
                    problem = HumanEvalProblem(
                        task_id=problem_data["task_id"],
                        prompt=problem_data["prompt"],
                        canonical_solution=problem_data["canonical_solution"],
                        test=problem_data["test"],
                        entry_point=problem_data["entry_point"],
                        description=problem_data.get("description")
                    )
                    problems[problem.task_id] = problem
        except FileNotFoundError:
            print(f"数据文件未找到: {data_path}")
            # 创建示例问题用于演示
            problems = self.create_sample_problems()
        
        return problems
    
    def create_sample_problems(self) -> Dict[str, HumanEvalProblem]:
        """创建示例问题（演示用）"""
        sample_problems = {
            "HumanEval/0": HumanEvalProblem(
                task_id="HumanEval/0",
                prompt='''def has_close_elements(numbers: List[float], threshold: float) -> bool:
    """ Check if in given list of numbers, are any two numbers closer to each other than
    given threshold.
    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
    False
    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
    True
    """
''',
                canonical_solution='''    for idx, elem in enumerate(numbers):
        for idx2, elem2 in enumerate(numbers):
            if idx != idx2:
                distance = abs(elem - elem2)
                if distance < threshold:
                    return True

    return False
''',
                test='''def check(candidate):
    assert candidate([1.0, 2.0, 3.0], 0.5) == False
    assert candidate([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True
    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True
    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False
    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True
    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False
    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True

check(has_close_elements)
''',
                entry_point="has_close_elements"
            )
        }
        return sample_problems
    
    def analyze_dataset(self):
        """分析数据集特征"""
        if not self.problems:
            return
        
        # 统计信息
        total_problems = len(self.problems)
        avg_prompt_length = sum(len(p.prompt) for p in self.problems.values()) / total_problems
        avg_solution_length = sum(len(p.canonical_solution) for p in self.problems.values()) / total_problems
        
        print(f"HumanEval数据集分析:")
        print(f"- 总问题数: {total_problems}")
        print(f"- 平均提示长度: {avg_prompt_length:.0f}字符")
        print(f"- 平均解决方案长度: {avg_solution_length:.0f}字符")
        
        # 分析问题类型
        self.categorize_problems()
    
    def categorize_problems(self):
        """问题分类分析"""
        categories = {
            "字符串处理": 0,
            "数学计算": 0,
            "列表操作": 0,
            "算法逻辑": 0,
            "数据结构": 0
        }
        
        for problem in self.problems.values():
            prompt_lower = problem.prompt.lower()
            
            if any(keyword in prompt_lower for keyword in ["string", "str", "char", "text"]):
                categories["字符串处理"] += 1
            elif any(keyword in prompt_lower for keyword in ["math", "sum", "product", "calculate"]):
                categories["数学计算"] += 1
            elif any(keyword in prompt_lower for keyword in ["list", "array", "sort", "filter"]):
                categories["列表操作"] += 1
            elif any(keyword in prompt_lower for keyword in ["tree", "graph", "node", "dict"]):
                categories["数据结构"] += 1
            else:
                categories["算法逻辑"] += 1
        
        print("\n问题类型分布:")
        for category, count in categories.items():
            percentage = count / len(self.problems) * 100
            print(f"- {category}: {count}个 ({percentage:.1f}%)")

import subprocess
import tempfile
import os
import signal
from concurrent.futures import ThreadPoolExecutor, TimeoutError
import multiprocessing

class SafeCodeExecutor:
    """安全的代码执行环境"""
    
    def __init__(self, timeout: int = 10, memory_limit: int = 128):
        self.timeout = timeout
        self.memory_limit = memory_limit  # MB
    
    def execute_code(self, code: str, test_code: str) -> Dict[str, Any]:
        """安全执行代码并运行测试"""
        # 合并代码和测试
        full_code = f"{code}\n\n{test_code}"
        
        # 创建临时文件
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
            f.write(full_code)
            temp_file = f.name
        
        try:
            # 使用subprocess执行代码
            result = self.run_with_timeout(temp_file)
            return result
        finally:
            # 清理临时文件
            os.unlink(temp_file)
    
    def run_with_timeout(self, file_path: str) -> Dict[str, Any]:
        """带超时的代码执行"""
        try:
            # 设置资源限制
            cmd = [
                "python3", "-c", 
                f"""
import resource
import sys

# 设置内存限制
resource.setrlimit(resource.RLIMIT_AS, ({self.memory_limit * 1024 * 1024}, {self.memory_limit * 1024 * 1024}))

# 设置CPU时间限制
resource.setrlimit(resource.RLIMIT_CPU, ({self.timeout}, {self.timeout}))

# 执行代码
with open('{file_path}', 'r') as f:
    exec(f.read())
"""
            ]
            
            process = subprocess.run(
                cmd,
                capture_output=True,
                text=True,
                timeout=self.timeout + 5  # 额外5秒缓冲
            )
            
            if process.returncode == 0:
                return {
                    "status": "passed",
                    "stdout": process.stdout,
                    "stderr": process.stderr,
                    "execution_time": None  # 需要实际测量
                }
            else:
                return {
                    "status": "failed",
                    "stdout": process.stdout,
                    "stderr": process.stderr,
                    "return_code": process.returncode
                }
        
        except subprocess.TimeoutExpired:
            return {
                "status": "timeout",
                "error": f"代码执行超时 ({self.timeout}秒)"
            }
        except Exception as e:
            return {
                "status": "error",
                "error": str(e)
            }
    
    def validate_syntax(self, code: str) -> Dict[str, Any]:
        """验证代码语法"""
        try:
            ast.parse(code)
            return {"valid": True}
        except SyntaxError as e:
            return {
                "valid": False,
                "error": f"语法错误: {e.msg} (第{e.lineno}行)",
                "lineno": e.lineno
            }
        except Exception as e:
            return {
                "valid": False,
                "error": f"解析错误: {str(e)}"
            }

class HumanEvalExecutor:
    """HumanEval专用执行器"""
    
    def __init__(self, dataset: HumanEvalDataset):
        self.dataset = dataset
        self.executor = SafeCodeExecutor()
    
    def evaluate_single_problem(self, task_id: str, generated_code: str) -> Dict[str, Any]:
        """评估单个问题的生成代码"""
        if task_id not in self.dataset.problems:
            return {"error": f"未找到问题: {task_id}"}
        
        problem = self.dataset.problems[task_id]
        
        # 验证语法
        syntax_check = self.executor.validate_syntax(generated_code)
        if not syntax_check["valid"]:
            return {
                "task_id": task_id,
                "status": "syntax_error",
                "error": syntax_check["error"],
                "passed": False
            }
        
        # 合并提示和生成的代码
        complete_code = problem.prompt + generated_code
        
        # 执行测试
        execution_result = self.executor.execute_code(complete_code, problem.test)
        
        result = {
            "task_id": task_id,
            "generated_code": generated_code,
            "execution_result": execution_result,
            "passed": execution_result["status"] == "passed"
        }
        
        return result
    
    def batch_evaluate(self, solutions: Dict[str, List[str]], k_values: List[int] = [1, 10, 100]) -> Dict[str, Any]:
        """批量评估多个解决方案"""
        results = {}
        
        for task_id, code_samples in solutions.items():
            task_results = []
            
            for i, code in enumerate(code_samples):
                result = self.evaluate_single_problem(task_id, code)
                result["sample_index"] = i
                task_results.append(result)
            
            results[task_id] = task_results
        
        # 计算pass@k指标
        pass_at_k_results = self.calculate_pass_at_k(results, k_values)
        
        return {
            "detailed_results": results,
            "pass_at_k": pass_at_k_results,
            "summary": self.generate_summary(results, pass_at_k_results)
        }
    
    def calculate_pass_at_k(self, results: Dict[str, List[Dict]], k_values: List[int]) -> Dict[int, float]:
        """计算pass@k指标"""
        pass_at_k = {}
        
        for k in k_values:
            total_problems = 0
            passed_problems = 0
            
            for task_id, task_results in results.items():
                if len(task_results) >= k:
                    total_problems += 1
                    # 检查前k个样本中是否有至少一个通过
                    k_samples = task_results[:k]
                    if any(result["passed"] for result in k_samples):
                        passed_problems += 1
            
            pass_at_k[k] = passed_problems / total_problems if total_problems > 0 else 0
        
        return pass_at_k
    
    def generate_summary(self, results: Dict, pass_at_k: Dict[int, float]) -> Dict[str, Any]:
        """生成评估摘要"""
        total_problems = len(results)
        total_samples = sum(len(task_results) for task_results in results.values())
        total_passed = sum(
            sum(1 for result in task_results if result["passed"]) 
            for task_results in results.values()
        )
        
        return {
            "total_problems": total_problems,
            "total_samples": total_samples,
            "total_passed": total_passed,
            "overall_success_rate": total_passed / total_samples if total_samples > 0 else 0,
            "pass_at_k_summary": {f"pass@{k}": f"{v:.1%}" for k, v in pass_at_k.items()}
        }

from abc import ABC, abstractmethod
import asyncio
import time
from concurrent.futures import ThreadPoolExecutor

class CodeGenerationModel(ABC):
    """代码生成模型抽象基类"""
    
    @abstractmethod
    def generate_code(self, prompt: str, num_samples: int = 1, temperature: float = 0.0) -> List[str]:
        """生成代码"""
        pass
    
    @abstractmethod
    def get_model_info(self) -> Dict[str, Any]:
        """获取模型信息"""
        pass

class OpenAICodeModel(CodeGenerationModel):
    """OpenAI代码生成模型"""
    
    def __init__(self, model_name: str = "gpt-4", api_key: str = None):
        self.model_name = model_name
        self.client = OpenAI(api_key=api_key)
    
    def generate_code(self, prompt: str, num_samples: int = 1, temperature: float = 0.0) -> List[str]:
        """生成代码样本"""
        responses = []
        
        for _ in range(num_samples):
            try:
                response = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=[
                        {
                            "role": "system",
                            "content": "你是一个专业的Python程序员。请根据给定的函数签名和文档字符串完成函数实现。只返回函数体代码，不要包含函数定义。"
                        },
                        {
                            "role": "user", 
                            "content": prompt
                        }
                    ],
                    temperature=temperature,
                    max_tokens=500,
                    stop=["def ", "class ", "import ", "from "]
                )
                
                code = response.choices[0].message.content.strip()
                responses.append(code)
                
            except Exception as e:
                print(f"代码生成错误: {e}")
                responses.append("    pass  # Error in generation")
        
        return responses
    
    def get_model_info(self) -> Dict[str, Any]:
        return {
            "model_name": self.model_name,
            "provider": "OpenAI",
            "type": "chat_completion"
        }

class HumanEvalBenchmark:
    """HumanEval基准测试主类"""
    
    def __init__(self, dataset_path: str = None):
        self.dataset = HumanEvalDataset(dataset_path)
        self.executor = HumanEvalExecutor(self.dataset)
        self.results_history = []
    
    def evaluate_model(self, 
                      model: CodeGenerationModel, 
                      num_samples_per_problem: int = 1,
                      temperature: float = 0.0,
                      k_values: List[int] = [1, 10, 100]) -> Dict[str, Any]:
        """评估单个模型"""
        
        print(f"开始评估模型: {model.get_model_info()['model_name']}")
        start_time = time.time()
        
        # 生成所有问题的解决方案
        solutions = {}
        
        for i, (task_id, problem) in enumerate(self.dataset.problems.items()):
            print(f"生成问题 {i+1}/{len(self.dataset.problems)}: {task_id}")
            
            try:
                code_samples = model.generate_code(
                    prompt=problem.prompt,
                    num_samples=num_samples_per_problem,
                    temperature=temperature
                )
                solutions[task_id] = code_samples
                
            except Exception as e:
                print(f"生成失败 {task_id}: {e}")
                solutions[task_id] = ["    pass  # Generation failed"] * num_samples_per_problem
        
        # 执行评估
        print("执行代码测试...")
        evaluation_results = self.executor.batch_evaluate(solutions, k_values)
        
        # 添加元数据
        evaluation_results["metadata"] = {
            "model_info": model.get_model_info(),
            "evaluation_time": time.time() - start_time,
            "num_samples_per_problem": num_samples_per_problem,
            "temperature": temperature,
            "timestamp": time.time()
        }
        
        # 保存到历史记录
        self.results_history.append(evaluation_results)
        
        return evaluation_results
    
    def compare_models(self, models: List[CodeGenerationModel], **eval_kwargs) -> Dict[str, Any]:
        """比较多个模型"""
        comparison_results = {}
        
        for model in models:
            model_name = model.get_model_info()["model_name"]
            results = self.evaluate_model(model, **eval_kwargs)
            comparison_results[model_name] = results
        
        # 生成比较报告
        comparison_report = self.generate_comparison_report(comparison_results)
        
        return {
            "individual_results": comparison_results,
            "comparison_report": comparison_report
        }
    
    def generate_comparison_report(self, results: Dict[str, Any]) -> Dict[str, Any]:
        """生成模型比较报告"""
        report = {
            "model_rankings": {},
            "performance_summary": {},
            "detailed_comparison": {}
        }
        
        # 提取pass@k结果
        model_scores = {}
        for model_name, result in results.items():
            pass_at_k = result["pass_at_k"]
            model_scores[model_name] = pass_at_k
            
            report["performance_summary"][model_name] = {
                "pass@1": f"{pass_at_k.get(1, 0):.1%}",
                "pass@10": f"{pass_at_k.get(10, 0):.1%}",
                "pass@100": f"{pass_at_k.get(100, 0):.1%}",
                "evaluation_time": f"{result['metadata']['evaluation_time']:.1f}s"
            }
        
        # 按pass@1排名
        if 1 in next(iter(model_scores.values()), {}):
            sorted_models = sorted(
                model_scores.items(),
                key=lambda x: x[1].get(1, 0),
                reverse=True
            )
            
            report["model_rankings"]["pass@1"] = [
                {"rank": i+1, "model": model, "score": f"{score.get(1, 0):.1%}"}
                for i, (model, score) in enumerate(sorted_models)
            ]
        
        return report
    
    def export_results(self, results: Dict[str, Any], output_path: str):
        """导出评估结果"""
        import json
        
        # 序列化结果
        serializable_results = self.make_serializable(results)
        
        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(serializable_results, f, ensure_ascii=False, indent=2)
        
        print(f"评估结果已导出至: {output_path}")
    
    def make_serializable(self, obj):
        """使对象可序列化"""
        if isinstance(obj, dict):
            return {k: self.make_serializable(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [self.make_serializable(item) for item in obj]
        elif hasattr(obj, '__dict__'):
            return self.make_serializable(obj.__dict__)
        else:
            return obj

# 使用示例
def main():
    # 初始化基准测试
    benchmark = HumanEvalBenchmark()
    
    # 创建模型实例
    models = [
        OpenAICodeModel("gpt-4"),
        OpenAICodeModel("gpt-3.5-turbo")
    ]
    
    # 评估模型
    results = benchmark.compare_models(
        models=models,
        num_samples_per_problem=10,
        temperature=0.2,
        k_values=[1, 5, 10]
    )
    
    # 导出结果
    benchmark.export_results(results, "humaneval_comparison.json")
    
    # 打印摘要
    for model_name, summary in results["comparison_report"]["performance_summary"].items():
        print(f"\n{model_name}:")
        for metric, value in summary.items():
            print(f"  {metric}: {value}")

if __name__ == "__main__":
    main()

import math
from scipy.special import comb

def calculate_pass_at_k_unbiased(n: int, c: int, k: int) -> float:
    """
    计算无偏的pass@k估计
    
    Args:
        n: 生成的样本总数
        c: 通过测试的样本数
        k: 考虑的样本数
    
    Returns:
        pass@k的无偏估计值
    """
    if n - c < k:
        return 1.0
    
    # 使用组合数学计算
    # pass@k = 1 - C(n-c, k) / C(n, k)
    return 1.0 - comb(n - c, k) / comb(n, k)

class PassAtKCalculator:
    """Pass@k指标计算器"""
    
    def __init__(self):
        self.cache = {}
    
    def calculate_problem_pass_at_k(self, results: List[bool], k: int) -> float:
        """计算单个问题的pass@k"""
        n = len(results)
        c = sum(results)
        
        if k > n:
            return 0.0
        
        if c == 0:
            return 0.0
        
        if c >= k:
            return 1.0
        
        return calculate_pass_at_k_unbiased(n, c, k)
    
    def calculate_dataset_pass_at_k(self, all_results: Dict[str, List[bool]], k: int) -> Dict[str, Any]:
        """计算整个数据集的pass@k"""
        problem_scores = {}
        total_score = 0
        
        for problem_id, results in all_results.items():
            score = self.calculate_problem_pass_at_k(results, k)
            problem_scores[problem_id] = score
            total_score += score
        
        avg_score = total_score / len(all_results) if all_results else 0
        
        return {
            "pass_at_k": avg_score,
            "k": k,
            "problem_scores": problem_scores,
            "total_problems": len(all_results),
            "distribution": self.analyze_score_distribution(problem_scores)
        }
    
    def analyze_score_distribution(self, scores: Dict[str, float]) -> Dict[str, Any]:
        """分析分数分布"""
        values = list(scores.values())
        
        if not values:
            return {}
        
        return {
            "mean": sum(values) / len(values),
            "median": sorted(values)[len(values) // 2],
            "min": min(values),
            "max": max(values),
            "perfect_score_count": sum(1 for v in values if v == 1.0),
            "zero_score_count": sum(1 for v in values if v == 0.0)
        }

def benchmark_comparison_2025():
    """2025年主流模型HumanEval表现比较"""
    
    # 基于搜索结果的2025年排行榜数据（示例）
    model_performance = {
        "Cursor+Claude 4.0": {"pass@1": 0.867, "domain": "科研领域"},
        "DeepSeek-Coder": {"pass@1": 0.825, "domain": "通用编程"},
        "GPT-4": {"pass@1": 0.813, "domain": "通用编程"},
        "Claude-3.5-Sonnet": {"pass@1": 0.792, "domain": "代码生成"},
        "GPT-3.5-Turbo": {"pass@1": 0.748, "domain": "轻量化"},
        "CodeLlama-34B": {"pass@1": 0.736, "domain": "开源模型"},
        "Llama-3.1-70B": {"pass@1": 0.712, "domain": "开源通用"},
    }
    
    print("2025年HumanEval排行榜 (Pass@1):")
    print("-" * 50)
    
    sorted_models = sorted(
        model_performance.items(),
        key=lambda x: x[1]["pass@1"],
        reverse=True
    )
    
    for i, (model, data) in enumerate(sorted_models, 1):
        print(f"{i:2d}. {model:<20} {data['pass@1']:.1%} ({data['domain']})")
    
    return model_performance

class HumanEvalErrorAnalyzer:
    """HumanEval错误分析器"""
    
    def __init__(self):
        self.error_categories = {
            "syntax_error": "语法错误",
            "runtime_error": "运行时错误",
            "logic_error": "逻辑错误",
            "timeout": "超时错误",
            "incomplete": "代码不完整"
        }
    
    def analyze_failures(self, evaluation_results: Dict[str, Any]) -> Dict[str, Any]:
        """分析失败案例"""
        failure_analysis = {
            "error_distribution": {},
            "problem_difficulty": {},
            "common_patterns": [],
            "improvement_suggestions": []
        }
        
        all_failures = []
        
        # 收集所有失败案例
        for task_id, task_results in evaluation_results["detailed_results"].items():
            failed_attempts = [r for r in task_results if not r["passed"]]
            
            for failure in failed_attempts:
                failure_info = {
                    "task_id": task_id,
                    "error_type": self.classify_error(failure),
                    "generated_code": failure.get("generated_code", ""),
                    "execution_result": failure.get("execution_result", {})
                }
                all_failures.append(failure_info)
        
        # 统计错误分布
        error_counts = {}
        for failure in all_failures:
            error_type = failure["error_type"]
            error_counts[error_type] = error_counts.get(error_type, 0) + 1
        
        failure_analysis["error_distribution"] = {
            error_type: {
                "count": count,
                "percentage": count / len(all_failures) * 100
            }
            for error_type, count in error_counts.items()
        }
        
        # 分析问题难度
        failure_analysis["problem_difficulty"] = self.analyze_problem_difficulty(
            evaluation_results["detailed_results"]
        )
        
        # 识别常见错误模式
        failure_analysis["common_patterns"] = self.identify_common_patterns(all_failures)
        
        return failure_analysis
    
    def classify_error(self, failure_result: Dict[str, Any]) -> str:
        """分类错误类型"""
        execution_result = failure_result.get("execution_result", {})
        
        if execution_result.get("status") == "syntax_error":
            return "syntax_error"
        elif execution_result.get("status") == "timeout":
            return "timeout"
        elif "stderr" in execution_result and execution_result["stderr"]:
            stderr = execution_result["stderr"].lower()
            if "syntaxerror" in stderr:
                return "syntax_error"
            elif any(error in stderr for error in ["nameerror", "attributeerror", "typeerror"]):
                return "runtime_error"
            else:
                return "runtime_error"
        elif execution_result.get("status") == "failed":
            return "logic_error"
        else:
            return "incomplete"
    
    def analyze_problem_difficulty(self, detailed_results: Dict[str, List[Dict]]) -> Dict[str, Any]:
        """分析问题难度"""
        problem_success_rates = {}
        
        for task_id, results in detailed_results.items():
            total_attempts = len(results)
            successful_attempts = sum(1 for r in results if r["passed"])
            success_rate = successful_attempts / total_attempts if total_attempts > 0 else 0
            
            problem_success_rates[task_id] = success_rate
        
        # 按难度分类
        difficulty_levels = {
            "简单": [tid for tid, rate in problem_success_rates.items() if rate > 0.8],
            "中等": [tid for tid, rate in problem_success_rates.items() if 0.4 <= rate <= 0.8],
            "困难": [tid for tid, rate in problem_success_rates.items() if rate < 0.4]
        }
        
        return {
            "success_rates": problem_success_rates,
            "difficulty_levels": difficulty_levels,
            "hardest_problems": sorted(
                problem_success_rates.items(),
                key=lambda x: x[1]
            )[:10]  # 最困难的10个问题
        }
    
    def identify_common_patterns(self, failures: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """识别常见错误模式"""
        patterns = []
        
        # 分析常见语法错误
        syntax_errors = [f for f in failures if f["error_type"] == "syntax_error"]
        if syntax_errors:
            patterns.append({
                "type": "syntax_patterns",
                "description": "常见语法错误",
                "count": len(syntax_errors),
                "examples": [f["execution_result"].get("stderr", "")[:100] for f in syntax_errors[:3]]
            })
        
        # 分析缩进问题
        indentation_errors = [
            f for f in failures 
            if "indentationerror" in f["execution_result"].get("stderr", "").lower()
        ]
        if indentation_errors:
            patterns.append({
                "type": "indentation_errors",
                "description": "缩进错误",
                "count": len(indentation_errors),
                "suggestion": "模型需要更好地处理Python缩进规则"
            })
        
        return patterns

class NextGenCodeEval:
    """下一代代码评估框架"""
    
    def __init__(self):
        self.evaluation_dimensions = {
            "functionality": "功能正确性",
            "efficiency": "执行效率",
            "readability": "代码可读性",
            "maintainability": "可维护性",
            "security": "安全性"
        }
    
    def comprehensive_evaluation(self, code: str, problem_spec: Dict) -> Dict[str, Any]:
        """综合评估代码质量"""
        results = {}
        
        # 功能正确性 (传统HumanEval方式)
        results["functionality"] = self.test_functionality(code, problem_spec)
        
        # 效率评估
        results["efficiency"] = self.analyze_efficiency(code)
        
        # 可读性评估
        results["readability"] = self.analyze_readability(code)
        
        # 安全性检查
        results["security"] = self.security_check(code)
        
        return results
    
    def analyze_efficiency(self, code: str) -> Dict[str, Any]:
        """分析代码效率"""
        # 这里可以集成复杂度分析工具
        return {
            "time_complexity": "估算时间复杂度",
            "space_complexity": "估算空间复杂度",
            "execution_time": "实际执行时间"
        }
    
    def analyze_readability(self, code: str) -> Dict[str, Any]:
        """分析代码可读性"""
        # 可以使用pylint、flake8等工具
        return {
            "style_score": 85,
            "naming_convention": "良好",
            "comment_coverage": "需要改进"
        }