Version 1
: Received: 16 May 2021 / Approved: 20 May 2021 / Online: 20 May 2021 (10:31:56 CEST)
How to cite:
Barzegar, A.; Barzegar, Y. Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product. Preprints2021, 2021050479. https://doi.org/10.20944/preprints202105.0479.v1
Barzegar, A.; Barzegar, Y. Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product . Preprints 2021, 2021050479. https://doi.org/10.20944/preprints202105.0479.v1
Barzegar, A.; Barzegar, Y. Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product. Preprints2021, 2021050479. https://doi.org/10.20944/preprints202105.0479.v1
APA Style
Barzegar, A., & Barzegar, Y. (2021). Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202105.0479.v1
Chicago/Turabian Style
Barzegar, A. and Yas Barzegar. 2021 "Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202105.0479.v1
Abstract
Computer systems are involved in many critical human applications today, so that a small error can lead to serious and dangerous problems. These errors can be from an error in the incorrect design of the user interface to an error in the program code. The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable Software according to the goals and needs of the organization makes measuring the quality of software products. an important issue for most organizations and institutions, To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured by Adaptive Neural Fuzzy Inference System. In recent years, powerful systems called fuzzy inference systems on The basis of adaptive neural network (ANFIS) has been used in various sciences. Using the power of neural network training and the linguistic advantage of fuzzy systems, these types of systems have been able to realize the advantages of the two in terms of analyzing very powerful complex processes. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study. It was applied at different levels to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the adaptive neural-fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.
Keywords
software quality, Adaptive Neural Fuzzy, ISO standard, quality model, Inference system
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
