research-article

Neural Network-based Detection of Self-Admitted Technical Debt: From Performance to Explainability

Authors:

Zhenchang Xing,

John GrundyAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 28, Issue 3

Article No.: 15, Pages 1 - 45

https://doi.org/10.1145/3324916

Published: 29 July 2019 Publication History

Abstract

Technical debt is a metaphor to reflect the tradeoff software engineers make between short-term benefits and long-term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text-mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length, and semantic variations, pose a big challenge to the accuracy of pattern or traditional text-mining-based SATD detection, especially for cross-project deployment. Furthermore, although traditional text-mining-based method outperforms pattern-based method in prediction accuracy, the text features it uses are less intuitive than human-summarized patterns, which makes the prediction results hard to explain. To improve the accuracy of SATD prediction, especially for cross-project prediction, we propose a Convolutional Neural Network-- (CNN) based approach for classifying code comments as SATD or non-SATD. To improve the explainability of our model’s prediction results, we exploit the computational structure of CNNs to identify key phrases and patterns in code comments that are most relevant to SATD. We have conducted an extensive set of experiments with 62,566 code comments from 10 open-source projects and a user study with 150 comments of another three projects. Our evaluation confirms the effectiveness of different aspects of our approach and its superior performance, generalizability, adaptability, and explainability over current state-of-the-art traditional text-mining-based methods for SATD classification.

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Cited By

Wang HGao ZBi TGrundy JWang XWu MYang X(2024)What Makes a Good TODO Comment?ACM Transactions on Software Engineering and Methodology10.1145/366481133:6(1-30)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3664811
Sutoyo ECapiluppi ASpinellis DConstantinou EBacchelli A(2024)SATDAUG - A Balanced and Augmented Dataset for Detecting Self-Admitted Technical DebtProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644880(289-293)Online publication date: 15-Apr-2024
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OBrien DBiswas SImtiaz SAbdalkareem RShihab ERajan HRoychoudhury APaiva AAbreu RStorey M(2024)Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub CopilotProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639176(1-13)Online publication date: 20-May-2024
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Index Terms

Neural Network-based Detection of Self-Admitted Technical Debt: From Performance to Explainability
1. Software and its engineering
  1. Software notations and tools
    1. Software maintenance tools

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 28, Issue 3

July 2019

278 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3343019

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Publication History

Published: 29 July 2019

Accepted: 01 March 2019

Revised: 01 March 2019

Received: 01 June 2018

Published in TOSEM Volume 28, Issue 3

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Cited By

Wang HGao ZBi TGrundy JWang XWu MYang X(2024)What Makes a Good TODO Comment?ACM Transactions on Software Engineering and Methodology10.1145/366481133:6(1-30)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3664811
Sutoyo ECapiluppi ASpinellis DConstantinou EBacchelli A(2024)SATDAUG - A Balanced and Augmented Dataset for Detecting Self-Admitted Technical DebtProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644880(289-293)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644880
OBrien DBiswas SImtiaz SAbdalkareem RShihab ERajan HRoychoudhury APaiva AAbreu RStorey M(2024)Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub CopilotProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639176(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639176
Gu HZhang SHuang QLiao ZLiu JLo D(2024)Self-Admitted Technical Debts Identification: How Far Are We?2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00087(804-815)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00087
Li HRajbahadur GLin DBezemer CJiang Z(2024)Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate OverfittingIEEE Access10.1109/ACCESS.2024.340254312(70676-70689)Online publication date: 2024
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Alhefdhi ADam HGhose A(2024)Towards automating self-admitted technical debt repaymentInformation and Software Technology10.1016/j.infsof.2023.107376167(107376)Online publication date: Mar-2024
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Liu HJiang SQi XQu YLi HLi TGuo CGuo S(2024)Detect software vulnerabilities with weight biases via graph neural networksExpert Systems with Applications10.1016/j.eswa.2023.121764238(121764)Online publication date: Mar-2024
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Rantala LMäntylä MLenarduzzi V(2024)Keyword-labeled self-admitted technical debt and static code analysis have significant relationship but limited overlapSoftware Quality Journal10.1007/s11219-023-09655-z32:2(391-429)Online publication date: 1-Jun-2024
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Xiao TZeng ZWang DHata HMcIntosh SMatsumoto K(2024)Quantifying and characterizing clones of self-admitted technical debt in build systemsEmpirical Software Engineering10.1007/s10664-024-10449-529:2Online publication date: 26-Feb-2024
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