stefanocarrera/autophagycode_D_he_train-mercury_Qwen3-8B_strategy_trust_t0.75_g4_run0_metrics

该数据集包含代码质量评估和测试相关的数据，用于分析编程任务的执行结果和代码复杂度。数据集包括164个训练样本，每个样本具有多个特征，如任务ID、入口点、可执行性、正确性、测试通过和失败数量、错误类型，以及代码复杂度指标（如Halstead词汇量、长度、体积、难度、努力程度和时间）、圈复杂度、可维护性指数、代码行数（LOC和SLOC）、注释百分比、类型标记比（TTR）、标记字典、香农熵、预测熵均值和最大值，以及定义函数数量。这些数据可用于评估代码性能、可读性和维护性。This dataset contains data related to code quality assessment and testing, used for analyzing the execution results and code complexity of programming tasks. It includes 164 training examples, with features such as task ID, entry point, executability, correctness, number of tests passed and failed, error type, and code complexity metrics (e.g., Halstead vocabulary, length, volume, difficulty, effort, and time), cyclomatic complexity, maintainability index, lines of code (LOC and SLOC), comment percentage, type-token ratio (TTR), token dictionary, Shannon entropy, mean and maximum predictive entropy, and number of functions defined. This data can be used to evaluate code performance, readability, and maintainability.