论文标题
探索代码样式在识别好程序员中的影响
Exploring the Impact of Code Style in Identifying Good Programmers
论文作者
论文摘要
代码样式是源代码中展示的美学选择，反映了程序员单个编码习惯。这项研究是第一个研究代码样式是否可以用作识别好程序员的指标。从Google Code Jam中选择了进行研究的数据。进行集群分析以查找特定的编码样式是否可以与好的程序员相关联。此外，使用风格功能对监督的机器学习模型进行了培训，并使用召回，Macro-F1，AUC-ROC和平衡的精度进行了评估，以预测好的程序员。结果表明，尽管没有特定的样式组可以归因于一种好的样式，但可以使用监督的机器学习模型来确定好的程序员。
Code style is an aesthetic choice exhibited in source code that reflects programmers individual coding habits. This study is the first to investigate whether code style can be used as an indicator to identify good programmers. Data from Google Code Jam was chosen for conducting the study. A cluster analysis was performed to find whether a particular coding style could be associated with good programmers. Furthermore, supervised machine learning models were trained using stylistic features and evaluated using recall, macro-F1, AUC-ROC and balanced accuracy to predict good programmers. The results demonstrate that good programmers may be identified using supervised machine learning models, despite that no particular style groups could be attributed as a good style.
