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On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem

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

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On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. / Lenarduzzi, Valentina; Kinnunen, Nyyti; Taibi, Davide.

2019 IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE, 2019. p. 98-107.

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

Harvard

Lenarduzzi, V, Kinnunen, N & Taibi, D 2019, On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. in 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE, pp. 98-107, IEEE/ACM International Conference on Technical Debt, 1/01/00. https://doi.org/10.1109/TechDebt.2019.00028

APA

Lenarduzzi, V., Kinnunen, N., & Taibi, D. (2019). On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. In 2019 IEEE/ACM International Conference on Technical Debt (TechDebt) (pp. 98-107). IEEE. https://doi.org/10.1109/TechDebt.2019.00028

Vancouver

Lenarduzzi V, Kinnunen N, Taibi D. On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. In 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE. 2019. p. 98-107 https://doi.org/10.1109/TechDebt.2019.00028

Author

Lenarduzzi, Valentina ; Kinnunen, Nyyti ; Taibi, Davide. / On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. 2019 IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE, 2019. pp. 98-107

Bibtex - Download

@inproceedings{ff27c7cd268444228c9bfe375a2f6bdc,
title = "On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem",
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.",
author = "Valentina Lenarduzzi and Nyyti Kinnunen and Davide Taibi",
year = "2019",
month = "8",
day = "5",
doi = "10.1109/TechDebt.2019.00028",
language = "English",
isbn = "978-1-7281-3372-0",
pages = "98--107",
booktitle = "2019 IEEE/ACM International Conference on Technical Debt (TechDebt)",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem

AU - Lenarduzzi, Valentina

AU - Kinnunen, Nyyti

AU - Taibi, Davide

PY - 2019/8/5

Y1 - 2019/8/5

N2 - 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.

AB - 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.

U2 - 10.1109/TechDebt.2019.00028

DO - 10.1109/TechDebt.2019.00028

M3 - Conference contribution

SN - 978-1-7281-3372-0

SP - 98

EP - 107

BT - 2019 IEEE/ACM International Conference on Technical Debt (TechDebt)

PB - IEEE

ER -