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dc.creatorSánchez Jerez, Ana Belénes
dc.creatorDelgado Pérez, Pedroes
dc.creatorMedina Bulo, Inmaculadaes
dc.creatorSegura Rueda, Sergioes
dc.date.accessioned2022-11-07T11:26:11Z
dc.date.available2022-11-07T11:26:11Z
dc.date.issued2022
dc.identifier.citationSánchez Jerez, A.B., Delgado Pérez, P., Medina Bulo, I. y Segura Rueda, S. (2022). Mutation testing in the wild: findings from GitHub. Empirical Software Engineering, 27 (6, art. nº132), 1-35. https://doi.org/10.1007/s10664-022-10177-8.
dc.identifier.issn1382-3256es
dc.identifier.issn1573-7616es
dc.identifier.urihttps://hdl.handle.net/11441/139077
dc.description.abstractMutation 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.es
dc.description.sponsorshipJunta de Andalucía US-1264651 (APOLO)es
dc.description.sponsorshipJunta de Andalucía P18-FR-2895 (EKIPMENT-PLUS)es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades RTI2018-101204-B-C21 (HORATIO)es
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades RTI2018-093608-BC33 (FAME)es
dc.formatapplication/pdfes
dc.format.extent35es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofEmpirical Software Engineering, 27 (6, art. nº132), 1-35.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMutation testinges
dc.subjectMutation toolses
dc.subjectData mininges
dc.subjectGitHubes
dc.titleMutation testing in the wild: findings from GitHubes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDUS-1264651 (APOLO)es
dc.relation.projectIDP18-FR-2895 (EKIPMENT-PLUS)es
dc.relation.projectIDRTI2018-101204-B-C21 (HORATIO)es
dc.relation.projectIDRTI2018-093608-BC33es
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s10664-022-10177-8es
dc.identifier.doi10.1007/s10664-022-10177-8es
dc.contributor.groupUniversidad de Sevilla. TIC-205: Ingeniería del Software Aplicadaes
dc.journaltitleEmpirical Software Engineeringes
dc.publication.volumen27es
dc.publication.issue6, art. nº132es
dc.publication.initialPage1es
dc.publication.endPage35es
dc.contributor.funderJunta de Andalucíaes
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (MICINN). Españaes

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