  When developers use different keywords such as TODO and FIXME in source code
comments to describe self-admitted technical debt (SATD), we refer it as
Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories
with 13,588 KL-SATD comments. We find that the median percentage of KL-SATD
comments among all comments is only 1,52%. We find that KL-SATD comment
contents 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 prior
work. Our machine learning classifier using logistic Lasso regression has good
performance in detecting KL-SATD comments (AUC-ROC 0.88). Finally, we
demonstrate that using machine learning we can identify comments that are
currently missing but which should have a SATD keyword in them. Automating SATD
identification of comments that lack SATD keywords can save time and effort by
replacing manual identification of comments. Using KL-SATD offers a potential
to bootstrap a complete SATD detector.
