A Neuro-Fuzzy based Automated System for Estimating Software Quality

A Neuro-Fuzzy based Automated System for Estimating Software Quality

© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Ritu, O. P. Sangwan
DOI :  10.14445/22315381/IJETT-V70I4P214

How to Cite?

Ritu, O. P. Sangwan, "A Neuro-Fuzzy based Automated System for Estimating Software Quality," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 164-173, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P214

In this increasing digital software era, various software becomes of daily use in human life, ranging from shopping to meeting, working from home, etc. It is a necessity for good software that is easy to operate, highly secure, and highly accurate. These properties constitute the quality of software. Generally, the quality of the software is estimated based on the expert`s opinion or from any other user of that software, which can be time-consuming and may not be highly accurate as it depends upon user-to-user experience. It is a demand for an automated system that the quality of software can be estimated by providing some inputs or features. Due to recent development in machine learning, the neural network has been largely employed in academia as well as industry. Thus, this paper presents a neuro-fuzzy-based automated system to estimate the quality of given software. The user needs to feed only five parameters, namely Reliability, Usability, Functionality, Efficiency, Portability, and Maintainability, and the proposed model automatically calculates the quality of software. The proposed model is based on the data collected from the 128 software, where 100 data-set are used for training the proposed neuro-fuzzy model and 28 data-set for testing purposes. The obtained results with the proposed approach closely match the actual software quality. Moreover, two fuzzy rule generation techniques, i.e. `grid partition` and `sub-clustering`, have been designed and compared the obtained results with both approaches. It is found that the proposed approach with sub-clustering has lesser error measures like MSE, MRE, and MARE in terms of performance indexes, among other methods.

ANFIS, Fuzzy logic, Neuro-fuzzy, Neural Networks, MATLAB Simulation, Software Quality.

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