stefanocarrera/autophagycode_D_he_train-mercury_Qwen3-0.6B_strategy_trust_t1_g3_metrics

该数据集包含164个训练样本，用于代码任务执行和质量评估。每个样本记录任务ID、入口点、可执行性、正确性、测试通过/失败数量、错误类型，以及代码复杂度度量（如Halstead度量、圈复杂度、可维护性指数、代码行数、注释比例等）。还包括词汇多样性、香农熵和预测熵等统计特征。数据集可能用于分析代码性能、错误检测或机器学习模型训练。

This dataset contains 164 training samples for code task execution and quality assessment. Each sample includes task ID, entry point, executability, correctness, number of tests passed/failed, error type, and code complexity metrics (e.g., Halstead metrics, cyclomatic complexity, maintainability index, lines of code, comment percentage). It also features statistical measures like vocabulary diversity, Shannon entropy, and predictive entropy. The dataset is likely used for analyzing code performance, error detection, or training machine learning models.