Clustered design-model generation from a program source code using chaos-based metaheuristic algorithms

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dc.authoridBahman Arasteh / 0000-0001-5202-6315
dc.authorscopusidBahman Arasteh / 39861139000en_US
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2022-10-31T10:43:27Z
dc.date.available2022-10-31T10:43:27Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractComprehension of the structure of software will facilitate maintaining the software more efficiently. Clustering software modules, as a reverse engineering technique, is assumed to be an effective technique in extracting comprehensible structural-models of software from the source code. Finding the best clustering model of a software system is regarded as a NP-complete problem. Minimizing the connections among the created clusters, maximizing the internal connections within the created clusters and maximizing the clustering quality are considered to be the most important objectives in software module clustering (SMC). Poor success rate, low stability and modularization quality are regarded as the major drawbacks of the previously proposed methods. In this paper, five different heuristic algorithms (Bat, Cuckoo, Teaching–Learning-Based, Black Widow and Grasshopper algorithms) are proposed for optimal clustering of software modules. Also, the effects of chaos theory in the performance of these algorithms in this problem have been experimentally investigated. The results of conducted experiments on the eight standard and real-world applications indicate that performance of the BWO, PSO, and TLB algorithms are higher than the other algorithms in SMC problem; also, the performance of these algorithm increased when their initial population were generated with logistic chaos method instead of random method. The average MQ of the generated clusters for the selected benchmark set by BWO, PSO and TLB are 3.155, 3.120 and 2.778, respectively.en_US
dc.identifier.citationArasteh, B. (2022). Clustered design-model generation from a program source code using chaos-based metaheuristic algorithms. Neural Computing and Applications, doi:10.1007/s00521-022-07781-6en_US
dc.identifier.doi10.1007/s00521-022-07781-6en_US
dc.identifier.scopus2-s2.0-85139475766en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07781-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3214
dc.identifier.wosWOS:000865380100003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArasteh, Bahman
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChaos Theoryen_US
dc.subjectHeuristic Algorithmsen_US
dc.subjectModularization Qualityen_US
dc.subjectModule Dependency Graphen_US
dc.subjectSoftware Modules Clusteringen_US
dc.titleClustered design-model generation from a program source code using chaos-based metaheuristic algorithmsen_US
dc.typeArticleen_US

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