Cite Article

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

Choose citation format

BibTeX

@article{IJASEIT9957,
   author = {Salama A. Mostafa and Saraswathy Shamini Gunasekaran and Shihab Hamad Khaleefah and Aida Mustapha and Mohammed Ahmed Jubair and Mustafa Hamid Hassan},
   title = {A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {6},
   year = {2019},
   pages = {2134--2141},
   keywords = {software requirements specifications; heuristic search; fuzzy logic; case-based reasoning; classification; similarity measurement.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9957},
   doi = {10.18517/ijaseit.9.6.9957}
}

EndNote

%A Mostafa, Salama A.
%A Gunasekaran, Saraswathy Shamini
%A Khaleefah, Shihab Hamad
%A Mustapha, Aida
%A Jubair, Mohammed Ahmed
%A Hassan, Mustafa Hamid
%D 2019
%T A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment
%B 2019
%9 software requirements specifications; heuristic search; fuzzy logic; case-based reasoning; classification; similarity measurement.
%! A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment
%K software requirements specifications; heuristic search; fuzzy logic; case-based reasoning; classification; similarity measurement.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9957
%R doi:10.18517/ijaseit.9.6.9957
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 6
%@ 2088-5334

IEEE

Salama A. Mostafa,Saraswathy Shamini Gunasekaran,Shihab Hamad Khaleefah,Aida Mustapha,Mohammed Ahmed Jubair and Mustafa Hamid Hassan,"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, pp. 2134-2141, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.6.9957.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mostafa, Salama A.
AU  - Gunasekaran, Saraswathy Shamini
AU  - Khaleefah, Shihab Hamad
AU  - Mustapha, Aida
AU  - Jubair, Mohammed Ahmed
AU  - Hassan, Mustafa Hamid
PY  - 2019
TI  - A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 6
Y2  - 2019
SP  - 2134
EP  - 2141
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - software requirements specifications; heuristic search; fuzzy logic; case-based reasoning; classification; similarity measurement.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9957
DO  - 10.18517/ijaseit.9.6.9957

RefWorks

RT Journal Article
ID 9957
A1 Mostafa, Salama A.
A1 Gunasekaran, Saraswathy Shamini
A1 Khaleefah, Shihab Hamad
A1 Mustapha, Aida
A1 Jubair, Mohammed Ahmed
A1 Hassan, Mustafa Hamid
T1 A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 6
YR 2019
SP 2134
OP 2141
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 software requirements specifications; heuristic search; fuzzy logic; case-based reasoning; classification; similarity measurement.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9957
DO  - 10.18517/ijaseit.9.6.9957