论文标题
检测技术债务的讨论
Detecting Discussions of Technical Debt
论文作者
论文摘要
技术债务（TD）是指在软件开发过程中的次优选择，以实现短期目标，而牺牲了长期质量。尽管开发人员经常非正式地讨论TD，但在描述大多数存储库中的问题时，该概念尚未将其结晶成一个始终应用的标签。我们将机器学习应用于在讨论问题跟踪器中的门票时，将开发人员的见解理解到TD中。我们生成的专家标签表明，在Chromium Issue Tracker中，在与每张票相关的免费文本中的讨论是否发生在与每张票相关的自由文本中。然后，我们使用这些标签来训练分类器，该分类器估算其余475,000张门票的标签。我们得出的结论是，对TD的讨论大约有16％的追踪铬问题出现。如果我们可以有效地对与TD相关的问题进行分类，那么我们可以专注于哪些实践对于及时解决方案最有用。
Technical debt (TD) refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although developers often informally discuss TD, the concept has not yet crystalized into a consistently applied label when describing issues in most repositories. We apply machine learning to understand developer insights into TD when discussing tickets in an issue tracker. We generate expert labels that indicate whether discussion of TD occurs in the free text associated with each ticket in a sample of more than 1,900 tickets in the Chromium issue tracker. We then use these labels to train a classifier that estimates labels for the remaining 475,000 tickets. We conclude that discussion of TD appears in about 16% of the tracked Chromium issues. If we can effectively classify TD-related issues, we can focus on what practices could be most useful for their timely resolution.
