A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

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dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridSefati, Seyed Salar/0000-0002-7208-3576
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridFratu, Octavian/0000-0001-5679-9307
dc.authorwosidKiani, Farzad/O-3363-2013
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidSefati, Seyed Salar/AAU-2556-2021
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidFratu, Octavian/AFK-0977-2022
dc.contributor.authorArasteh, Bahman
dc.contributor.authorArasteh, Keyvan
dc.contributor.authorKiani, Farzad
dc.contributor.authorSefati, Seyed Salar
dc.contributor.authorFratu, Octavian
dc.contributor.authorHalunga, Simona
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:39:26Z
dc.date.available2024-05-19T14:39:26Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.en_US
dc.description.sponsorshipMarie Sklstrok;odowska Curie Actions (MSCA) Innovative Training Network (ITN)en_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.3390/math12020186
dc.identifier.issn2227-7390
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85183096320en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.3390/math12020186
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4777
dc.identifier.volume12en_US
dc.identifier.wosWOS:001151438900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofMathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBioinspired Testing Methoden_US
dc.subjectDiscretized Bat Optimization Algorithmen_US
dc.subjectBranch Coverageen_US
dc.subjectStabilityen_US
dc.subjectSuccess Rateen_US
dc.titleA Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithmen_US
dc.typeArticleen_US

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