Abstract
Self-Admitted Technical Debt or SATD can be found in various sources, such as source code comments, commit messages, issue tracking systems, and pull requests. Previous research has established the existence of relations between SATD items in different sources; such relations can be useful for investigating and improving SATD management. However, there is currently a lack of approaches for automatically detecting these SATD relations. To address this, we proposed and evaluated approaches for automatically identifying SATD relations across different sources. Our findings show that our approach outperforms baseline approaches by a large margin, achieving an average F1-score of 0.829 in identifying relations between SATD items. Moreover, we explored the characteristics of SATD relations in 103 open-source projects and describe nine major cases in which related SATD is documented in a second source, and give a quantitative overview of 26 kinds of relations.
Original language | English |
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Title of host publication | Proceedings - 2023 ACM/IEEE International Conference on Technical Debt, TechDebt 2023 |
Publisher | IEEE |
Pages | 11-21 |
Number of pages | 11 |
ISBN (Electronic) | 9798350311945 |
DOIs | |
Publication status | Published - 10-Aug-2023 |
Event | 6th ACM/IEEE International Conference on Technical Debt, TechDebt 2023 - Melbourne, Australia Duration: 14-May-2023 → 15-May-2023 |
Publication series
Name | Proceedings - 2023 ACM/IEEE International Conference on Technical Debt, TechDebt 2023 |
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Conference
Conference | 6th ACM/IEEE International Conference on Technical Debt, TechDebt 2023 |
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Country/Territory | Australia |
City | Melbourne |
Period | 14/05/2023 → 15/05/2023 |
Keywords
- code comments
- commit messages
- deep learning
- issue tracking systems
- pull requests
- SATD duplication
- SATD repayment
- self-admitted technical debt
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Replication Package for Automatically Identifying Relations Between Self-Admitted Technical Debt Across Different Sources
Li, Y. (Creator), Soliman, M. (Creator) & Avgeriou, P. (Creator), ZENODO, 11-Apr-2023
Dataset