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A fuzzy case-based reasoning model for software requirements specifications quality assessment

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dc.citedby3
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorKhaleefah S.H.en_US
dc.contributor.authorMustapha A.en_US
dc.contributor.authorJubair M.A.en_US
dc.contributor.authorHassan M.H.en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57188929678en_US
dc.contributor.authorid57200530694en_US
dc.contributor.authorid57203690245en_US
dc.contributor.authorid57193264476en_US
dc.date.accessioned2023-05-29T07:28:13Z
dc.date.available2023-05-29T07:28:13Z
dc.date.issued2019
dc.description.abstractDifferent software Quality Assurance (SQA) audit techniques are applied in the literature to determine whether the required standards and procedures within the Software Requirements Specification (SRS) phase are adhered to. The inspection of the Software Requirements Specification (iSRS) system is an analytical assurance tool which is proposed to strengthen the ability to scrutinize how to optimally create high-quality SRSs. The iSRS utilizes a Case-Based Reasoning (CBR) model in carrying out the SRS quality analysis based on the experience of the previously analyzed cases. This paper presents the contribution of integrating fuzzy Logic technique in the CBR steps to form a Fuzzy Case-Based Reasoning (FCBR) model for improving the reasoning and accuracy of the iSRS system. Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. The input to the nearest cases algorithm is the relevant cases and the output is the nearest cases. The fuzzy Logic technique works on the selected nearest cases and it utilizes similarity measurement methods to classify the cases into no-match, partial-match and complete-match cases. The features matching results assist the revised step of the CBR to generate a new solution. The implementation of the new FCBR model shows that converting numerical representation to qualitative terms simplifies the matching process and improves the decision-making of the system. � Insight Society.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18517/ijaseit.9.6.9957
dc.identifier.epage2141
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85078068693
dc.identifier.spage2134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078068693&doi=10.18517%2fijaseit.9.6.9957&partnerID=40&md5=79ceac463e2f9033014ee8ecece8cd8c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24877
dc.identifier.volume9
dc.publisherInsight Societyen_US
dc.sourceScopus
dc.sourcetitleInternational Journal on Advanced Science, Engineering and Information Technology
dc.titleA fuzzy case-based reasoning model for software requirements specifications quality assessmenten_US
dc.typeArticleen_US
dspace.entity.typePublication
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