A systematic review on software reliability prediction via swarm intelligence algorithms

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dc.authoridJasser, Muhammed Basheer/0000-0001-5292-465X
dc.contributor.authorKong, Li Sheng
dc.contributor.authorJasser, Muhammed Basheer
dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorMohamed, Ali Wagdy
dc.date.accessioned2024-10-12T19:42:55Z
dc.date.available2024-10-12T19:42:55Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThe widespread integration of software into all parts of our lives has led to the need for software of higher reliability. Ensuring reliable software usually necessitates some form of formal methods in the early stages of the development process which requires strenuous effort. Hence, researchers in the field of software reliability introduced Software Reliability Growth Models (SRGMs) as a relatively inexpensive approach to software reliability prediction. Conventional parameter estimation methods of SRGMs were ineffective and left more to be desired. Consequently, researchers sought out swarm intelligence to combat its flaws, resulting in significant improvements. While similar surveys exist within the domain, the surveys are broader in scope and do not cover many swarm intelligence algorithms. Moreover, the broader scope has resulted in the occasional omission of information regarding the design for reliability predictions. A more comprehensive survey containing 38 studies and 18 different swarm intelligence algorithms in the domain is presented. Each design proposed by the studies was systematically analyzed where relevant information including the measures used, datasets used, SRGMs used, and the effectiveness of each proposed design, were extracted and organized into tables and taxonomies to be able to identify the current trends within the domain. Some notable findings include the distance-based approach providing a high prediction accuracy and an increasing trend in hybridized variants of swarm intelligence algorithms designs to predict software reliability. Future researchers are encouraged to include Mean Square Error (MSE) or Root MSE as the measures offer the largest sample size for comparison.en_US
dc.description.sponsorshipSunway Universityen_US
dc.description.sponsorshipThe authors present their appreciation to Sunway University for funding the publication of this research through Sunway University Publication Support Scheme.en_US
dc.identifier.doi10.1016/j.jksuci.2024.102132
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85199493338en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2024.102132
dc.identifier.urihttps://hdl.handle.net/11467/8664
dc.identifier.volume36en_US
dc.identifier.wosWOS:001283590000001en_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.snmzWoS_2024en_US
dc.subjectSwarm Intelligence Algorithmsen_US
dc.subjectSoftware Reliability Predictionen_US
dc.subjectSoftware Reliability Growth Modelsen_US
dc.subjectEvolutionary Algorithmsen_US
dc.titleA systematic review on software reliability prediction via swarm intelligence algorithmsen_US
dc.typeReview Articleen_US

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