Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916550195281920 |
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| author | Sohn, Jeongju Soremekun, Ezekiel Papadakis, Michail |
| author_facet | Sohn, Jeongju Soremekun, Ezekiel Papadakis, Michail |
| contents | In this paper we apply mutation testing in an in-time fashion, i.e., across multiple project releases. Thus, we investigate how the mutants of the current version behave in the future versions of the programs. We study the characteristics of what we call latent mutants, i.e., the mutants that are live in one version and killed in later revisions, and explore whether they are predictable with these properties. We examine 131,308 mutants generated by Pitest on 13 open-source projects. Around 11.2% of these mutants are live, and 3.5% of them are latent, manifesting in 104 days on average. Using the mutation operators and change-related features we successfully demonstrate that these latent mutants are identifiable, predicting them with an accuracy of 86% and a balanced accuracy of 67% using a simple random forest classifier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution Sohn, Jeongju Soremekun, Ezekiel Papadakis, Michail Software Engineering In this paper we apply mutation testing in an in-time fashion, i.e., across multiple project releases. Thus, we investigate how the mutants of the current version behave in the future versions of the programs. We study the characteristics of what we call latent mutants, i.e., the mutants that are live in one version and killed in later revisions, and explore whether they are predictable with these properties. We examine 131,308 mutants generated by Pitest on 13 open-source projects. Around 11.2% of these mutants are live, and 3.5% of them are latent, manifesting in 104 days on average. Using the mutation operators and change-related features we successfully demonstrate that these latent mutants are identifiable, predicting them with an accuracy of 86% and a balanced accuracy of 67% using a simple random forest classifier. |
| title | Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2501.01873 |