Large Language Models (LLMs) for unsupervised code correctness evaluation have recently gained attention because they can judge if code runs as intended without requiring reference implementations or unit tests, which may be unavailable, sparse, or unreliable. However, most prior approaches condition LLM evaluators directly on the full code implementation, forcing the model to jointly infer program behavior and evaluate correctness in a single step. This entanglement leads to misinterpretations of code behavior and unreliable judgments. To mitigate this issue, we introduce CoCoA, an unsupervised Code Comprehension then Auditing framework that first comprehends functionality to generate a natural-language explanation. Then it evaluates task alignment based on this explanation. By sequentially sampling comprehension before evaluation, CoCoA improves the quality of inferred program behavior and enables the evaluator to focus on behavioral alignment rather than raw implementation details. Across multiple datasets, programming languages, and models, CoCoA achieves up to $68\%$ increased F1 score and up to $20\%$ increased accuracy over the best-performing baselines.

翻译：大型语言模型（LLMs）在无监督代码正确性评估领域近期备受关注，因其无需参考实现或单元测试（这些资源可能不可用、稀疏或不可靠）即可判断代码是否按预期运行。然而，现有方法大多直接基于完整代码实现来约束LLM评估器，迫使模型在单一步骤中联合推断程序行为并评估正确性。这种耦合导致对代码行为的误解和不可靠的判断。为解决此问题，我们提出CoCoA——一种无监督的代码理解后再审计框架：首先通过理解代码功能生成自然语言解释，随后基于该解释评估任务对齐度。通过顺序采样理解过程再执行评估，CoCoA提升了推断程序行为的质量，使评估器能聚焦于行为对齐而非原始实现细节。在多个数据集、编程语言和模型上的实验表明，CoCoA相较最优基准方法的F1分数提升高达68%，准确率提升高达20%。