A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code

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dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridArasteh, Keyvan/0000-0002-2041-6439
dc.authorwosidKiani, Farzad/O-3363-2013
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidTorkamanian-Afshar, Mahsa/AAD-9989-2022
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.accessioned2024-05-19T14:46:06Z
dc.date.available2024-05-19T14:46:06Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
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. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.identifier.doi10.1016/j.jksuci.2023.101655
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85165894761en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.jksuci.2023.101655
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5440
dc.identifier.volume35en_US
dc.identifier.wosWOS:001079494100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_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 effective structural model of a program source codeen_US
dc.typeArticleen_US

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