Program Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithm

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dc.authoridRasheed, Jawad/0000-0003-3761-1641
dc.authoridAbu-Mahfouz, Adnan M./0000-0002-6413-3924
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridSeyyedabbasi, Amir/0000-0001-5186-4499
dc.authorwosidRasheed, Jawad/AAY-5207-2020
dc.authorwosidAbu-Mahfouz, Adnan M./S-2253-2017
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidSeyyedabbasi, Amir/HJH-7387-2023
dc.contributor.authorArasteh, Bahman
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorRasheed, Jawad
dc.contributor.authorAbu-Mahfouz, Adnan M.
dc.date.accessioned2024-05-19T14:46:44Z
dc.date.available2024-05-19T14:46:44Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractOne of expensive stages of the software lifecycle is its maintenance. Software maintenance will be much simpler if its structural models are available. Software module clustering is thought to be a practical reverse engineering method for building software structural models from source code. The most crucial goals in software module clustering are to minimize connections between created clusters, maximize internal connections within clusters, and maximize clustering quality. It is thought that finding the best software clustering model is an NP-complete task. The key shortcomings of the earlier techniques are their low success rates, low stability, and insufficient modularization quality. In this paper, for effective clustering of software source code, a discretized sand cat swarm optimization (SCSO) algorithm has been proposed. The proposed method takes the dependency graph of the source code and generates the best clusters for it. Ten standard and real-world benchmarks were used to assess the performance of the suggested approach. The outcomes show that the quality of clustering is improved when a discretized SCSO algorithm was used to address the software module clustering issue. The suggested method beats the previous heuristic approaches in terms of modularization quality, convergence speed, and success rate.en_US
dc.identifier.doi10.3390/sym15020401
dc.identifier.issn2073-8994
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85149201033en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.3390/sym15020401
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5582
dc.identifier.volume15en_US
dc.identifier.wosWOS:000941348100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSymmetry-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSoftware Module Clusteringen_US
dc.subjectCohesionen_US
dc.subjectCouplingen_US
dc.subjectModularization Qualityen_US
dc.subjectSand Cat Swarm Optimization Algorithmen_US
dc.titleProgram Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithmen_US
dc.typeArticleen_US

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