A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

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dc.authoridAlipour-Banaei, Hamed/0000-0003-0146-1450
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.authoridSoleimanian Gharehchopogh, Farhad/0000-0003-1588-1659
dc.authoridGhanbarzadeh, Reza/0000-0001-9073-1576
dc.authorwosidAlipour-Banaei, Hamed/G-9892-2016
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGunes, Peri
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorBanaei, Hamed Alipour
dc.contributor.authorGhanbarzadeh, Reza
dc.date.accessioned2024-05-19T14:51:16Z
dc.date.available2024-05-19T14:51:16Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractMaintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software's source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method's effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.en_US
dc.identifier.doi10.1155/2023/3988288
dc.identifier.issn1076-2787
dc.identifier.issn1099-0526
dc.identifier.scopus2-s2.0-85181944776en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1155/2023/3988288
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5908
dc.identifier.volume2023en_US
dc.identifier.wosWOS:001137343200002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWiley-Hindawien_US
dc.relation.ispartofComplexityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectSoftwareen_US
dc.titleA Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clusteringen_US
dc.typeArticleen_US

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