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Technical debt is a metaphor indicating sub-optimal solutions implemented forshort-term benefits by sacrificing the long-term maintainability andevolvability of software. A special type of technical debt is explicitlyadmitted by software engineers (e.g. using a TODO comment); this is calledSelf-Admitted Technical Debt or SATD. Most work on automatically identifyingSATD focuses on source code comments. In addition to source code comments,issue tracking systems have shown to be another rich source of SATD, but thereare no approaches specifically for automatically identifying SATD in issues. Inthis paper, we first create a training dataset by collecting and manuallyanalyzing 4,200 issues (that break down to 23,180 sections of issues) fromseven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase,Impala, and Thrift) using two popular issue tracking systems (i.e., Jira andGoogle Monorail). We then propose and optimize an approach for automaticallyidentifying SATD in issue tracking systems using machine learning. Our findingsindicate that: 1) our approach outperforms baseline approaches by a wide marginwith regard to the F1-score; 2) transferring knowledge from suitable datasetscan improve the predictive performance of our approach; 3) extracted SATDkeywords are intuitive and potentially indicating types and indicators of SATD;4) projects using different issue tracking systems have less common SATDkeywords compared to projects using the same issue tracking system; 5) a smallamount of training data is needed to achieve good accuracy.

Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning