Detecting and explaining Self-Admitted Technical Debts with Attention-based neural networks

Xin Wang, Jin Liu, Li Li, Xiao Chen, Xiao Liu, Hao Wu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Self-Admitted Technical Debt (SATD) is a sub-type of technical debt. It is introduced to represent such technical debts that are intentionally introduced by developers in the process of software development. While being able to gain short-term benefits, the introduction of SATDs often requires to be paid back later with a higher cost, e.g., introducing bugs to the software or increasing the complexity of the software. To cope with these issues, our community has proposed various machine learning-based approaches to detect SATDs. These approaches, however, are either not generic that usually require manual feature engineering efforts or do not provide promising means to explain the predicted outcomes. To that end, we propose to the community a novel approach, namely HATD (Hybrid Attention-based method for self-admitted Technical Debt detection), to detect and explain SATDs using attention-based neural networks. Through extensive experiments on 445, 365 comments in 20 projects, we show that HATD is effective in detecting SATDs on both in-the-lab and in-the-wild datasets under both within-project and cross-project settings. HATD also outperforms the state-of-the-art approaches in detecting and explaining SATDs.

Original languageEnglish
Title of host publicationProceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020
EditorsClaire Le Goues, David Lo
Place of PublicationNew York NY USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages871-882
Number of pages12
ISBN (Electronic)9781450367684
DOIs
Publication statusPublished - 2020
EventAutomated Software Engineering Conference 2020 - Virtual, Melbourne, Australia
Duration: 21 Sep 202025 Sep 2020
Conference number: 35th
https://dl.acm.org/doi/proceedings/10.1145/3324884 (Proceedings)
https://conf.researchr.org/home/ase-2020 (Website)
https://dl.acm.org/doi/proceedings/10.1145/3417113 (Proceedings)

Conference

ConferenceAutomated Software Engineering Conference 2020
Abbreviated titleASE 2020
CountryAustralia
CityMelbourne
Period21/09/2025/09/20
Internet address

Keywords

  • Attention-based Neural Networks
  • SATD
  • Self-Admitted Technical Debt
  • Word Embedding

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