autophagycode_D_metrics_he_Qwen3-14B_lr0.0001_correct_g9

该数据集包含与代码执行和质量评估相关的结构化信息，旨在支持代码分析、错误检测和软件质量评估任务。数据集由164个训练样本组成，总计18729字节。每个样本包含14个特征字段，涵盖任务标识（task_id）、执行入口点（entry_point）、可执行状态（is_executable）、正确性标记（is_correct）、通过/失败的测试数量（tests_passed/tests_failed）、测试运行时间（test_run_time_ms）、错误类型（error_type）等执行信息，以及Halstead复杂度指标（包括词汇量、长度、体积、难度和工作量）和可维护性指数（maintainability_index）。数据集适用于代码质量分析、自动化测试验证和软件度量研究等场景。This dataset contains structured information related to code execution and quality assessment, aiming to support tasks such as code analysis, bug detection, and software quality evaluation. The dataset comprises 164 training samples with a total size of 18729 bytes. Each sample includes 14 feature fields, covering execution-related information including task identifier (task_id), execution entry point (entry_point), executable status (is_executable), correctness flag (is_correct), count of passed/failed tests (tests_passed/tests_failed), test run time (test_run_time_ms), error type (error_type), as well as Halstead complexity metrics (comprising vocabulary, length, volume, difficulty, and effort) and maintainability index (maintainability_index). This dataset is applicable to scenarios including code quality analysis, automated test validation, and software metrics research.