Systems Analysis and Design (SIG SAND)
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Paper Type
Complete
Paper Number
1416
Description
Context: Technical debt (TD) contextualizes the problem of pending development tasks as a type of debt that brings a short-term benefit to the project, but which may have to be paid with interest later. When development teams explicitly admit these issues, TD is named Self-Admitted Technical Debt (SATD). Objective: Evaluate a SATD identification strategy, based on a SATD conceptual model implemented by the eXcomment tool, identifying comment patterns that are good indicators of the presence of SATD and those that point to false positive items. Method: We conducted an empirical study, considering two large and mature open-source software projects. Results: We identified 25 patterns that are more commonly found in code comments that point to SATD items and patterns that point to false-positive comments, indicating that those patterns can be excluded from the SATD conceptual model. Conclusion: We provide new evidence on how software engineers can use code comments to detect SATD items automatically.
Recommended Citation
Farias, Mário; Mendes, Thiago Souto; Mendonça, Manoel G.; and Spinola, Rodrigo O., "On Comment Patterns that are Good Indicators of the Presence of Self-Admitted Technical Debt and those that Lead to False Positive Items" (2021). AMCIS 2021 Proceedings. 5.
https://aisel.aisnet.org/amcis2021/sig_sand/sig_sand/5
On Comment Patterns that are Good Indicators of the Presence of Self-Admitted Technical Debt and those that Lead to False Positive Items
Context: Technical debt (TD) contextualizes the problem of pending development tasks as a type of debt that brings a short-term benefit to the project, but which may have to be paid with interest later. When development teams explicitly admit these issues, TD is named Self-Admitted Technical Debt (SATD). Objective: Evaluate a SATD identification strategy, based on a SATD conceptual model implemented by the eXcomment tool, identifying comment patterns that are good indicators of the presence of SATD and those that point to false positive items. Method: We conducted an empirical study, considering two large and mature open-source software projects. Results: We identified 25 patterns that are more commonly found in code comments that point to SATD items and patterns that point to false-positive comments, indicating that those patterns can be excluded from the SATD conceptual model. Conclusion: We provide new evidence on how software engineers can use code comments to detect SATD items automatically.
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