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When developers use different keywords such as TODO and FIXME in source codecomments to describe self-admitted technical debt (SATD), we refer it asKeyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositorieswith 13,588 KL-SATD comments. We find that the median percentage of KL-SATDcomments among all comments is only 1,52%. We find that KL-SATD commentcontents 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 priorwork. Our machine learning classifier using logistic Lasso regression has goodperformance in detecting KL-SATD comments (AUC-ROC 0.88). Finally, wedemonstrate that using machine learning we can identify comments that arecurrently missing but which should have a SATD keyword in them. Automating SATDidentification of comments that lack SATD keywords can save time and effort byreplacing manual identification of comments. Using KL-SATD offers a potentialto bootstrap a complete SATD detector.

Prevalence, Contents and Automatic Detection of KL-SATD