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dc.contributor.authorDelgado-Pérez, Pedro
dc.contributor.authorMedina Bulo, Inmaculada
dc.contributor.otherIngeniería Informáticaes_ES
dc.date.accessioned2020-07-28T10:41:17Z
dc.date.available2020-07-28T10:41:17Z
dc.date.issued2018-12
dc.identifier.issn0950-5849
dc.identifier.urihttp://hdl.handle.net/10498/23499
dc.description.abstractContext: Search-based techniques have been applied to almost all areas in software engineering, especially to software testing, seeking to solve hard optimization problems. However, the problem of selecting mutants to improve the test suite at a lower cost has not been explored to the same extent as other problems, such as mutant selection for test suite evaluation or test data generation. Objective: In this paper, we apply search-based mutant selection to enhance the quality of test suites efficiently. Namely, we use the technique known as Evolutionary Mutation Testing (EMT), which allows reducing the number of mutants while preserving the power to refine the test suite. Despite reported benefits of its application, the existing empirical results were derived from a limited number of case studies, a particular set of mutation operators and a vague measure, which currently makes it difficult to determine the real performance of this technique. Method: This paper addresses the shortcomings of previous studies, providing a new methodology to evaluate EMT on the basis of the actual improvement of the test suite achieved by using the evolutionary strategy. We make use of that methodology in new experiments with a carefully selected set of real-world C++ case studies. Results: EMT shows a good performance for most case studies and levels of demand of test suite improvement (around 45% less mutants than random selection in the best case). The results reveal that even a reduced subset of mutants selected with EMT can serve to increase confidence in the test suite, especially in programs with a large set of mutants. Conclusions: These results support the use of search-based techniques to solve the problem of mutant selection for a more efficient test suite refinement. Additionally, we identify some aspects that could foreseeably help enhance EMT.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceInformation and Software Technology, Volume 104, 2018, Pages 130-143.es_ES
dc.subjectsearch-based software engineeringes_ES
dc.subjectmutation testinges_ES
dc.subjectevolutionaryalgorithmes_ES
dc.subjectgenetic algorithmes_ES
dc.titleSearch-Based Mutant Selection for Efficient Test Suite Improvement: Evaluation and Resultses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.infsof.2018.07.011


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
This work is under a Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 Internacional