Automatically Identifying Relations Between Self-Admitted Technical Debt Across Different Sources
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 13 Mar 2023.
Builds on
6 of 21 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
- 2019How to Fine-Tune BERT for Text Classification?cited by 409
- 2022Automatic Identification of Self-Admitted Technical Debt from Four Different Sourcescited by 9
- 2022Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learningcited by 6
- 2022Self-Admitted Technical Debt in the Embedded Systems Industry: An Exploratory Case Studycited by 1
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Citing papers per year: 2023: 1.
1 paper.