Technical debt is a metaphor indicating sub-optimal solutions implemented for
short-term benefits by sacrificing the long-term maintainability and
evolvability of software. A special type of technical debt is explicitly
admitted by software engineers (e.g. using a TODO comment); this is called
Self-Admitted Technical Debt or SATD. Most work on automatically identifying
SATD focuses on source code comments. In addition to source code comments,
issue tracking systems have shown to be another rich source of SATD, but there
are no approaches specifically for automatically identifying SATD in issues. In
this paper, we first create a training dataset by collecting and manually
analyzing 4,200 issues (that break down to 23,180 sections of issues) from
seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase,
Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and
Google Monorail). We then propose and optimize an approach for automatically
identifying SATD in issue tracking systems using machine learning. Our findings
indicate that: 1) our approach outperforms baseline approaches by a wide margin
with regard to the F1-score; 2) transferring knowledge from suitable datasets
can improve the predictive performance of our approach; 3) extracted SATD
keywords are intuitive and potentially indicating types and indicators of SATD;
4) projects using different issue tracking systems have less common SATD
keywords compared to projects using the same issue tracking system; 5) a small
amount of training data is needed to achieve good accuracy.

通过机器学习方法，本文针对问题跟踪系统中的自认技术债务 (Self-Admitted Technical Debt, SATD) 提出自动识别方案，并使用两种流行的跟踪系统 (Jira 和 Google Monorail) 收集和手动分析来自七个开源项目的 4,200 个问题。研究结果表明，我们的方法比基线方法在 F1 得分方面表现出更好的性能，并且所提取的 SATD 关键字是直观的，可能表明了 SATD 的类型和指标。