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dc.contributor.authorArasteh, Bahman
dc.contributor.authorSadegi, Razieh
dc.contributor.authorArasteh, Keyvan
dc.contributor.authorGunes, Peri
dc.contributor.authorKiani, Farzad
dc.contributor.authorTorkamanian-Afshar, Mahsa
dc.date.accessioned2023-08-11T07:56:12Z
dc.date.available2023-08-11T07:56:12Z
dc.date.issued2023en_US
dc.identifier.citationARASTEH, Bahman, Razieh SADEGI, Keyvan ARASTEH, Peri GUNES, Farzad KIANI & Mahsa TORKAMANIAN-AFSHAR. "A Bioinspired Discrete Heuristic Algorithm to Generate the Eeffective Structural Model of a Program Source Code". Journal of King Saud University – Computer and Information Sciences, 35.8 (2023): 1-17.en_US
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85165894761&origin=SingleRecordEmailAlert&dgcid=raven_sc_search_en_us_email&txGid=df784e950b83422db20ce1bddd64f90a
dc.identifier.urihttps://hdl.handle.net/11352/4635
dc.description.abstractWhen the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches’ main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.jksuci.2023.101655en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectOlympiad optimization algorithmen_US
dc.subjectSoftware module clusteringen_US
dc.subjectCohesionen_US
dc.subjectModularization qualityen_US
dc.titleA Bioinspired Discrete Heuristic Algorithm to Generate the Eeffective Structural Model of a Program Source Codeen_US
dc.typearticleen_US
dc.relation.journalJournal of King Saud University – Computer and Information Sciencesen_US
dc.contributor.departmentFSM Vakıf Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume35en_US
dc.identifier.issue8en_US
dc.identifier.startpage1en_US
dc.identifier.endpage17en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorKiani, Farzad


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