Savalan: multi objective and homogeneous method for software modules clustering

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dc.authoridBahman Arasteh / 0000-0001-5202-6315
dc.authoridFarzad Kiani / 0000-0002-0354-9344
dc.authorscopusidBahman Arasteh / 39861139000en_US
dc.authorscopusidFarzad Kiani / 36662461100en_US
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.authorwosidFarzad Kiani / O-3363-2013
dc.contributor.authorArasteh, Bahman
dc.contributor.authorFatolahzadeh, Ahmad
dc.contributor.authorKiani, Farzad
dc.date.accessioned2021-12-01T06:08:59Z
dc.date.available2021-12-01T06:08:59Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractReverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi-objective and NP-hard problem; maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other. Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high-quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.en_US
dc.description.versionWOS:000720303600001en_US
dc.identifier.citationArasteh, B., Fatolahzadeh, A., & Kiani, F. Savalan: Multi objective and homogeneous method for software modules clustering. Journal of Software: Evolution and Process, e2408.en_US
dc.identifier.doi10.1002/smr.2408en_US
dc.identifier.issn2047-7481en_US
dc.identifier.scopus2-s2.0-85119450766en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1002/smr.2408
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2291
dc.identifier.wosWOS:000720303600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArasteh, Bahman
dc.institutionauthorKiani, Farzad
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofJournal of Software: Evolution and Processen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHomogenous Clusteringen_US
dc.subjectModule Clusteringen_US
dc.subjectMulti Objective Genetic Algorithmen_US
dc.subjectSoftware Maintenanceen_US
dc.titleSavalan: multi objective and homogeneous method for software modules clusteringen_US
dc.typeArticleen_US

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