A Novel Metaheuristic Based Method for Software Mutation Test Using the Discretized and Modified Forrest Optimization Algorithm

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dc.authoridGharehchopogh, Farhad Soleimanian/0000-0003-1588-1659
dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authorwosidGharehchopogh, Farhad Soleimanian/AAX-9598-2020
dc.authorwosidKiani, Farzad/O-3363-2013
dc.authorwosidTorkamanian-Afshar, Mahsa/AAD-9989-2022
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorGunes, Peri
dc.contributor.authorKiani, Farzad
dc.contributor.authorTorkamanian-Afshar, Mahsa
dc.date.accessioned2024-05-19T14:46:44Z
dc.date.available2024-05-19T14:46:44Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe number of detected bugs by software test data determines the efficacy of the test data. One of the most important topics in software engineering is software mutation testing, which is used to evaluate the efficiency of software test methods. The syntactical modifications are made to the program source code to make buggy (mutated) programs, and then the resulting mutants (buggy programs) along with the original programs are executed with the test data. Mutation testing has several drawbacks, one of which is its high computational cost. Higher execution time of mutation tests is a challenging problem in the software engineering field. The major goal of this work is to reduce the time and cost of mutation testing. Mutants are inserted in each instruction of a program using typical mutation procedures and tools. Meanwhile, in a real-world program, the likelihood of a bug occurrence in the simple and non-bug-prone sections of a program is quite low. According to the 80-20 rule, 80 percent of a program's bugs are discovered in 20% of its fault-prone code. The first stage of the suggested solution uses a discretized and modified version of the Forrest optimization algorithm to identify the program's most bug-prone paths; the second stage injects mutants just in the identified bug-prone instructions and data. In the second step, the mutation operators are only injected into the identified instructions and data that are bug-prone. Studies on standard benchmark programs have shown that the proposed method reduces about 27.63% of the created mutants when compared to existing techniques. If the number of produced mutants is decreased, the cost of mutation testing will also decrease. The proposed method is independent of the platform and testing tool. The results of the experiments confirm that the use of the proposed method in each testing tool such as Mujava, Muclipse, Jester, and Jumble makes a considerable mutant reduction.en_US
dc.identifier.doi10.1007/s10836-023-06070-x
dc.identifier.endpage370en_US
dc.identifier.issn0923-8174
dc.identifier.issn1573-0727
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85162226699en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage347en_US
dc.identifier.urihttps://doi.org10.1007/s10836-023-06070-x
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5583
dc.identifier.volume39en_US
dc.identifier.wosWOS:001015504200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Electronic Testing-Theory and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSoftware Mutation Testen_US
dc.subjectBug-Prone Codesen_US
dc.subjectForest Optimization Algorithmen_US
dc.subjectMutation Scoreen_US
dc.titleA Novel Metaheuristic Based Method for Software Mutation Test Using the Discretized and Modified Forrest Optimization Algorithmen_US
dc.typeArticleen_US

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