DOI : https://doi.org/10.18517/ijaseit.9.6.9957

A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment

Salama A. Mostafa (1), Saraswathy Shamini Gunasekaran (2), Shihab Hamad Khaleefah (3), Aida Mustapha (4), Mohammed Ahmed Jubair (5), Mustafa Hamid Hassan (6)
(1) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia
(2) College of Computing and Informatics, Universiti Tenaga Nasional
(3) Faculty of Computer Science, Al Maarif University College
(4) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia
(6) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia
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How to cite (IJASEIT) :
Mostafa, Salama A., et al. “A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 2134-41, doi:10.18517/ijaseit.9.6.9957.
Citation Format :
Different 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.

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