KL-SATD的流行、含量及自动检测
Prevalence, Contents and Automatic Detection of KL-SATD
摘要：
当开发人员在源代码注释中使用不同的关键字（如TODO和FIXME）来描述自我承认的技术债务（SATD）时，我们将其称为关键字标记的SATD（KL-SATD）。我们研究了来自33个软件库的KL-SATD，其中包含13，588条KL-SATD评论。我们发现KL-SATD评论在所有评论中的百分比中位数仅为1.52%。我们发现KL-SATD的注释内容包括表示代码更改和不确定性的词语，如remove、fix、maybe和probably。这使得他们与其他评论不同。KL-SATD注释内容类似于先前工作的手动标记的SATD注释。我们使用逻辑Lasso回归的机器学习分类器在检测KL-SATD评论方面具有良好的性能（AUC-ROC 0.88）。最后，我们证明了使用机器学习，我们可以识别当前缺少的评论，但其中应该有一个SATD关键字。自动SATD识别缺少SATD关键字的注释可以取代手动识别注释，从而节省时间和精力。使用KL-SATD提供了一个潜在的引导一个完整的SATD检测器。
英文摘要：
When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories with 13,588 KL-SATD comments. We find that the median percentage of KL-SATD comments among all comments is only 1,52%. We find that KL-SATD comment contents include words expressing code changes and uncertainty, such as remove, fix, maybe and probably. This makes them different compared to other comments. KL-SATD comment contents are similar to manually labeled SATD comments of prior work. Our machine learning classifier using logistic Lasso regression has good performance in detecting KL-SATD comments (AUC-ROC 0.88). Finally, we demonstrate that using machine learning we can identify comments that are currently missing but which should have a SATD keyword in them. Automating SATD identification of comments that lack SATD keywords can save time and effort by replacing manual identification of comments. Using KL-SATD offers a potential to bootstrap a complete SATD detector.
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