University of Oulu

L. Rantala, M. Mäntylä and D. Lo, "Prevalence, Contents and Automatic Detection of KL-SATD," 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, 2020, pp. 385-388, doi: 10.1109/SEAA51224.2020.00069

Prevalence, contents and automatic detection of KL-SATD

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Author: Rantala, Leevi1; Mäntylä, Mika1; Lo, David2
Organizations: 1ITEE, M3S, University of Oulu, Oulu, Finland
2Information Systems, Singapore Management University, Singapore, Singapore
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020120198835
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-12-01
Description:

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.

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ISBN: 978-1-7281-9532-2
ISBN Print: 978-1-7281-9533-9
Pages: 385 - 388
DOI: 10.1109/SEAA51224.2020.00069
OADOI: https://oadoi.org/10.1109/SEAA51224.2020.00069
Host publication: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
Conference: Euromicro Conference on Software Engineering and Advanced Applications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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