research-article

Mutation testing in the wild: findings from GitHub

Authors:

Ana B. Sánchez,

Pedro Delgado-Pérez,

Inmaculada Medina-Bulo,

Sergio SeguraAuthors Info & Claims

Empirical Software Engineering, Volume 27, Issue 6

https://doi.org/10.1007/s10664-022-10177-8

Published: 01 November 2022 Publication History

Abstract

Mutation testing exploits artificial faults to measure the adequacy of test suites and guide their improvement. It has become an extremely popular testing technique as evidenced by the vast literature, numerous tools, and research events on the topic. Previous survey papers have successfully compiled the state of research, its evolution, problems, and challenges. However, the use of mutation testing in practice is still largely unexplored. In this paper, we report the results of a thorough study on the use of mutation testing in GitHub projects. Specifically, we first performed a search for mutation testing tools, 127 in total, and we automatically searched the GitHub repositories including evidence of their use. Then, we focused on the top ten most widely used tools, based on the previous results, and manually revised and classified over 3.5K GitHub active repositories importing them. Among other findings, we observed a recent upturn in interest and activity, with Infection (PHP), PIT (Java) and Humbug (PHP) being the most widely used mutation tools in recent years. The predominant use of mutation testing is development, followed by teaching and learning, and research projects, although with significant differences among mutation tools found in the literature—less adopted and largely used in teaching and research—and those found in GitHub only—more popular and more widely used in development. Our work provides a new and encouraging perspective on the state of practice of mutation testing.

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Cited By

Ustunboyacioglu IKumara IDi Nucci DTamburri DVan Den Heuvel W(2024)Data Quality Assessment in the Wild: Findings from GitHubProceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering10.1145/3661167.3661213(120-129)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3661167.3661213
Wang JChen STang ZLin PWang Y(2024)False Positives and Deceptive Errors in SQL Assessment: A Large-Scale Analysis of Online Judge SystemsACM Transactions on Computing Education10.1145/365467724:3(1-23)Online publication date: 28-Mar-2024
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Wang ZYu MFeng YZhang W(2024)Do we need high-order mutation in fault-based Boolean-specification testing?Journal of Systems and Software10.1016/j.jss.2023.111933210:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111933
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Mutation testing in the wild: findings from GitHub
1. General and reference
2. Software and its engineering

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cover image Empirical Software Engineering

Empirical Software Engineering Volume 27, Issue 6

Nov 2022

1651 pages

ISSN:1382-3256

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© The Author(s) 2022.

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Kluwer Academic Publishers

United States

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Published: 01 November 2022

Accepted: 16 May 2022

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European Commission (ERDF)
Junta de Andalucía (APOLO Project)
Junta de Andalucía (EKIPMENT-PLUS)
Spanish Government (HORATIO project)
Spanish Government (FAME project)
Universidad de Sevilla

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Ustunboyacioglu IKumara IDi Nucci DTamburri DVan Den Heuvel W(2024)Data Quality Assessment in the Wild: Findings from GitHubProceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering10.1145/3661167.3661213(120-129)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3661167.3661213
Wang JChen STang ZLin PWang Y(2024)False Positives and Deceptive Errors in SQL Assessment: A Large-Scale Analysis of Online Judge SystemsACM Transactions on Computing Education10.1145/365467724:3(1-23)Online publication date: 28-Mar-2024
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