论文标题
用慢跑者确定自我吸引的技术债务：一种两步的方法
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
论文作者
论文摘要
跟踪和管理自我吸引的技术债务（SATD）对于维护健康软件项目很重要。这需要人类专家的大量时间和精力来手动识别SATD。当前的自动化解决方案的精度没有令人满意的精度，并在识别SATD时回忆以完全自动化该过程。为了解决上述问题，我们提出了一个两步框架，称为jitterbug，用于识别SATD。 Jitterbug首先使用新型的模式识别技术自动识别自动以接近100％精度的“易于找到”的SATD。随后，采用机器学习技术来帮助人类专家手动以减少人类努力来识别剩余的“难以找到” SATD。我们对十个软件项目的仿真研究表明，与先前的最新方法相比，JitterBug可以更有效地识别SATD（人为努力）。
Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the "easy to find" SATDs automatically with close to 100% precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining "hard to find" SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.
