research-article

On the diffuseness of code technical debt in Java projects of the apache ecosystem

Authors:

Nyyti Saarimäki,

Valentina Lenarduzzi,

Davide TaibiAuthors Info & Claims

TechDebt '19: Proceedings of the Second International Conference on Technical Debt

Pages 98 - 107

https://doi.org/10.1109/TechDebt.2019.00028

Published: 26 May 2019 Publication History

Abstract

Background. Companies commonly invest major effort into removing, respectively not introducing, technical debt issues detected by static analysis tools such as SonarQube, Cast, or Coverity. These tools classify technical debt issues into categories according to severity, and developers commonly pay attention to not introducing issues with a high level of severity that could generate bugs or make software maintenance more difficult.

Objective. In this work, we aim to understand the diffuseness of Technical Debt (TD) issues and the speed with which developers remove them from the code if they introduced such an issue. The goal is to understand which type of TD is more diffused and how much attention is paid by the developers, as well as to investigate whether TD issues with a higher level of severity are resolved faster than those with a lower level of severity. We conducted a case study across 78K commits of 33 Java projects from the Apache Software Foundation Ecosystem to investigate the distribution of 1.4M TD items.

Results. TD items introduced into the code are mostly related to code smells (issues that can increase the maintenance effort). Moreover, developers commonly remove the most severe issues faster than less severe ones. However, the time needed to resolve issues increases when the level of severity increases (minor issues are removed faster that blocker ones).

Conclusion. One possible answer to the unexpected issue of resolution time might be that severity is not correctly defined by the tools. Another possible answer is that the rules at an intermediate severity level could be the ones that technically require more time to be removed. The classification of TD items, including their severity and type, require thorough investigation from a research point of view.

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Cited By

Graf-Vlachy LWagner SCodabux ZGalster MSpinola R(2024)Different Debt: An Addition to the Technical Debt Dataset and a Demonstration Using Developer PersonalityProceedings of the 7th ACM/IEEE International Conference on Technical Debt10.1145/3644384.3644475(31-35)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644384.3644475
Kozanidis NVerdecchia RGuzman E(2022)Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack OverflowProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546245(45-56)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546245
Tan JFeitosa DAvgeriou P(2022)Does it matter who pays back Technical Debt? An empirical study of self-fixed TDInformation and Software Technology10.1016/j.infsof.2021.106738143:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.infsof.2021.106738
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On the diffuseness of code technical debt in Java projects of the apache ecosystem

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cover image ACM Conferences

TechDebt '19: Proceedings of the Second International Conference on Technical Debt

May 2019

137 pages

Program Chairs:
Paris Avgeriou
University of Groningen, The Netherlands
,
Klaus Schmid
Stiftung University Hildesheim, Germany

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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IEEE Press

Publication History

Published: 26 May 2019

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ICSE '19

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ICSE '19: 41st International Conference on Software Engineering

May 26 - 27, 2019

Quebec, Montreal, Canada

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Overall Acceptance Rate 14 of 31 submissions, 45%

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Cited By

Graf-Vlachy LWagner SCodabux ZGalster MSpinola R(2024)Different Debt: An Addition to the Technical Debt Dataset and a Demonstration Using Developer PersonalityProceedings of the 7th ACM/IEEE International Conference on Technical Debt10.1145/3644384.3644475(31-35)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644384.3644475
Kozanidis NVerdecchia RGuzman E(2022)Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack OverflowProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546245(45-56)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546245
Tan JFeitosa DAvgeriou P(2022)Does it matter who pays back Technical Debt? An empirical study of self-fixed TDInformation and Software Technology10.1016/j.infsof.2021.106738143:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.infsof.2021.106738
Tan JFeitosa DAvgeriou PIzurieta CGalster MFelderer M(2020)An empirical study on self-fixed technical debtProceedings of the 3rd International Conference on Technical Debt10.1145/3387906.3388621(11-20)Online publication date: 28-Jun-2020
https://dl.acm.org/doi/10.1145/3387906.3388621
Rantala LRothermel GBae D(2020)Towards better technical debt detection with NLP and machine learning methodsProceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings10.1145/3377812.3381404(242-245)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377812.3381404
Lenarduzzi VSaarimäki NTaibi DMinku LKhomh FPetrić J(2019)The Technical Debt DatasetProceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3345629.3345630(2-11)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3345629.3345630

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