Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2020

Abstract

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.

Keywords

data mining, Natural language processing, self-admitted technical debt

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): 26-28 August, Slovenia: Proceedings

First Page

385

Last Page

388

ISBN

9781728195322

Identifier

10.1109/SEAA51224.2020.00069

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1109/SEAA51224.2020.00069

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