research-article

Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems

Authors:

Fabio Ferreira,

Marco Tulio ValenteAuthors Info & Claims

MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories

Pages 137 - 146

https://doi.org/10.1145/3379597.3387459

Published: 18 September 2020 Publication History

Abstract

Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in source code comments. By contrast, in this paper we argue that developers can admit technical debt by other means, e.g., by creating issues in tracking systems and labelling them as referring to TD. We refer to this type of SATD as issue-based SATD or just SATD-I. We study a sample of 286 SATD-I instances collected from five open source projects, including Microsoft Visual Studio and GitLab Community Edition. We show that only 29% of the studied SATD-I instances can be tracked to source code comments. We also show that SATD-I issues take more time to be closed, compared to other issues, although they are not more complex in terms of code churn. Besides, in 45% of the studied issues TD was introduced to ship earlier, and in almost 60% it refers to DESIGN flaws. Finally, we report that most developers pay SATD-I to reduce its costs or interests (66%). Our findings suggest that there is space for designing novel tools to support technical debt management, particularly tools that encourage developers to create and label issues containing TD concerns.

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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
Show More Cited By

Index Terms

Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software evolution

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cover image ACM Conferences

MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories

June 2020

675 pages

ISBN:9781450375177

DOI:10.1145/3379597

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 September 2020

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MSR '20: 17th International Conference on Mining Software Repositories

June 29 - 30, 2020

Seoul, Republic of Korea

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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
Sheikhaei MTian YWang SXu B(2024)An empirical study on the effectiveness of large language models for SATD identification and classificationEmpirical Software Engineering10.1007/s10664-024-10548-329:6Online publication date: 1-Oct-2024
https://doi.org/10.1007/s10664-024-10548-3
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
https://doi.org/10.1007/s10664-024-10449-5
Skryseth DShivashankar KPilán IMartini A(2023)Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers2023 ACM/IEEE International Conference on Technical Debt (TechDebt)10.1109/TechDebt59074.2023.00017(92-101)Online publication date: May-2023
https://doi.org/10.1109/TechDebt59074.2023.00017
Wilder GMiyamoto RWatson SKazman RPeruma A(2023)An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps2023 ACM/IEEE International Conference on Technical Debt (TechDebt)10.1109/TechDebt59074.2023.00007(1-10)Online publication date: May-2023
https://doi.org/10.1109/TechDebt59074.2023.00007
Li YSoliman MAvgeriou PSomers L(2023)Self-Admitted Technical Debt in the Embedded Systems Industry: An Exploratory Case StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.322437849:4(2545-2565)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TSE.2022.3224378
Fairbanks JTharigonda AEisty N(2023)Analyzing the Effects of CI/CD on Open Source Repositories in GitHub and GitLab2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA57763.2023.10197778(176-181)Online publication date: 23-May-2023
https://doi.org/10.1109/SERA57763.2023.10197778
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