Prevalence, Contents and Automatic Detection of KL-SATD

• Main Authors: RANTALA, Leevi, MANTYLA, Mika, LO, David
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2020
• 主題: data mining; Natural language processing; self-admitted technical debt; Software Engineering
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/5624; https://ink.library.smu.edu.sg/context/sis_research/article/6627/viewcontent/KL_SATD_2020_pv.pdf
• 機構: Singapore Management University

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-...