Fuzuli: Automatic Test Data Generation for Software Structural Testing using Grey Wolf Optimization Algorithm and Genetic Algorithm

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dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.contributor.authorSattari, Mohammad Reza
dc.contributor.authorKalan, Reza Shokri
dc.date.accessioned2024-05-19T14:47:00Z
dc.date.available2024-05-19T14:47:00Z
dc.date.issued2022
dc.departmentİstinye Üniversitesien_US
dc.descriptionIEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) -- SEP 12-15, 2022 -- Falerna, ITALYen_US
dc.description.abstractSoftware testing refers to a process that improves the quality of software systems through bug detection, but software testing is one of the time and cost-consuming stages in software development. Hence, software test automation is regarded as a solution, which can facilitate heavy and laborious tasks of testing. Problem: Automatic generation of data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic algorithms have been proposed for this problem. Failure to maximise branch coverage, the poor success rate in optimal test data generation, and low stable results are the major demerits of the previous methods. Goal: Enhancing the branch coverage rate of the generated test data, enhancing the success rate in generating the test data with maximum coverage, and enhancing the stability and speed criteria are the main goals of this study. Method: In this study, a combination of grey wolf optimization algorithm and genetic algorithm have been used to automatically generate optimal test data. The proposed hybrid method (Fuzuli(1)) tries to generate test data with maximum branch coverage at the software source code level. Results: The results obtained from the proposed algorithm were compared with those of the following algorithms: Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).The results obtained from running a wide range of tests on standard benchmark programs showed that the proposed algorithm outperforms other algorithms with an average coverage of %99.98, a success rate of %99.97, and an average output of 2.86.en_US
dc.description.sponsorshipIstinye university; Digiturk beIN Media Groupen_US
dc.description.sponsorshipThis work is supported by Istinye university and Digiturk beIN Media Group (https://www.digiturk.com.tr/).en_US
dc.identifier.doi10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927968
dc.identifier.endpage199en_US
dc.identifier.isbn978-1-6654-6297-6
dc.identifier.scopus2-s2.0-85145347229en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage194en_US
dc.identifier.urihttps://doi.org10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927968
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5633
dc.identifier.wosWOS:000948109800029en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2022 Ieee Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (Dasc/Picom/Cbdcom/Cyberscitech)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectTest Data Generationen_US
dc.subjectBranch Coverageen_US
dc.subjectStabilityen_US
dc.subjectGwoen_US
dc.titleFuzuli: Automatic Test Data Generation for Software Structural Testing using Grey Wolf Optimization Algorithm and Genetic Algorithmen_US
dc.typeConference Objecten_US

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