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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Parul Gandhi1, Mohammad Zubair Khan2, Ravi Kumar Sharma3, Omar H. Alhazmi2, Surbhi Bhatia4,*, Chinmay Chakraborty5

1 Department of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, 121006, India
2 Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
3 Department of Computer Applications, Chandigarh Group of Colleges, Landran, 140307, India
4 College of Computer Sciences and Information Technology, King Faisal University, Saudi Arabia
5 Department of Electronics & Communication Engineering, Birla Institute of Technology, Mesra Jharkhand, India

* Corresponding Author: Surbhi Bhatia. Email: email

Computer Systems Science and Engineering 2022, 41(3), 891-902.


Software reliability is the primary concern of software development organizations, and the exponentially increasing demand for reliable software requires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of these errors helps the organization improve and enhance the software’s reliability and save money, time, and effort. Many soft computing techniques are available to get solutions for critical problems but selecting the appropriate technique is a big challenge. This paper proposed an efficient algorithm that can be used for the prediction of software reliability. The proposed algorithm is implemented using a hybrid approach named Neuro-Fuzzy Inference System and has also been applied to test data. In this work, a comparison among different techniques of soft computing has been performed. After testing and training the real time data with the reliability prediction in terms of mean relative error and mean absolute relative error as 0.0060 and 0.0121, respectively, the claim has been verified. The results claim that the proposed algorithm predicts attractive outcomes in terms of mean absolute relative error plus mean relative error compared to the other existing models that justify the reliability prediction of the proposed model. Thus, this novel technique intends to make this model as simple as possible to improve the software reliability.


Cite This Article

APA Style
Gandhi, P., Khan, M.Z., Sharma, R.K., Alhazmi, O.H., Bhatia, S. et al. (2022). Software reliability assessment using hybrid neuro-fuzzy model. Computer Systems Science and Engineering, 41(3), 891-902.
Vancouver Style
Gandhi P, Khan MZ, Sharma RK, Alhazmi OH, Bhatia S, Chakraborty C. Software reliability assessment using hybrid neuro-fuzzy model. Comput Syst Sci Eng. 2022;41(3):891-902
IEEE Style
P. Gandhi, M.Z. Khan, R.K. Sharma, O.H. Alhazmi, S. Bhatia, and C. Chakraborty "Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model," Comput. Syst. Sci. Eng., vol. 41, no. 3, pp. 891-902. 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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