Examining the Impact of Self-Admitted Technical Debt on Software Quality
Abstract
Technical debt refers to incomplete or temporary workarounds that allow us to speed software development in the short term at the cost of paying a higher price later on. Recently, studies have shown that technical debt can be detected from source code comments, referred to as self-admitted technical debt. Researchers have examined the detection, classification and removal of self-admitted technical debt. However, to date there is no empirical evidence on the impact of self-admitted technical debt on software quality. Therefore, in this paper, we examine the relation between self-admitted technical debt and software quality by investigating whether (i) files with self-admitted technical debt have more defects compared to files without self-admitted technical debt, (ii) whether self-admitted technical debt changes introduce future defects, and (iii) whether self-admitted technical debt-related changes tend to be more difficult. We measured the difficulty of a change using well-known measures proposed in prior work such as the amount of churn, the number of files, the number of modified modules in a change, as well as the entropy of a change. An empirical study using five open source projects, namely Hadoop, Chromium, Cassandra, Spark and Tomcat, showed that: (i) there is no clear trend when it comes to defects and self-admitted technical debt, although the defectiveness of the technical debt files increases after the introduction of technical debt, (ii) self-admitted technical…
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Files affected by technical debt tend to be more defect-prone.
“files affected by technical debt tend to be more defect-prone [24].”
From Self-Admitted GenAI Usage in Open-Source Software (2026) · cites this paper as [24]
Cites
Kruchten, P., Nord, R. L., Ozkaya, I., & Falessi, D. (2013) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/2507288.2507326 · Cited by 87 · see citing works
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Self-Admitted GenAI Usage in Open-Source Software
Xiao, T., Fan, Y., Calefato, F., Treude, C., Kula, R. G., Hata, H., & Baltes, S. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3681886 · Cited by 1 · see citing works
- Authors
- Wehaibi, S., Shihab, E., & Guerrouj, L.
- Affiliations
- Concordia University (CA)
- École de Technologie Supérieure (CA)
- ORCID
- Shihab, Emad
- Venue
- 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 179-188
- OpenAlex
- W2395955025
- Cited by
- 114
- References
- 1
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 17 · 2023: 14 · 2022: 23 · 2021: 15 · 2020: 11 · 2019: 8 · 2018: 6 · 2017: 7 · 2016: 1
- FWCI
- 6.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- self-admitted technical debt, technical debt, software quality, fault prediction, code comments, customer churn, open source software, software maintainability
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