Agile Software Quality of Adaptability Risk Measurement using Fuzzy Inference System

2018 ◽  
Vol 6 (9) ◽  
pp. 755-759 ◽  
Author(s):  
Anand Kumar Rai ◽  
Shalini Agrawal ◽  
Mazahar Khaliq
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Risk Measurement ◽  
Inference System ◽  
Agile Software

scholarly journals Measuring Software Quality Product Based on Fuzzy Inference System Techniques in ISO Standard

2021 ◽  
Author(s):  
Atrin Barzegar
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Iso Standard ◽  
Software Products ◽  
Software Product ◽  
The Impact

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. 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, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion 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 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.

scholarly journals Adaptive Neuro-Fuzzy Inference System for Measuring software quality Product

2021 ◽  
Author(s):  
Atrin Barzegar ◽  
Yas Barzegar
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Software Products ◽  
Software Product ◽  
Neural Fuzzy

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.

Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system

2011 ◽  
Vol 11 (3) ◽  
pp. 3238-3246 ◽  
Author(s):  
Jinquan Wan ◽  
Mingzhi Huang ◽  
Yongwen Ma ◽  
Wenjie Guo ◽  
Yan Wang ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Paper Mill ◽  
Adaptive Network ◽  
Inference System ◽  
Effluent Quality ◽  
Paper Mill Wastewater

scholarly journals Perancangan Alat Pendeteksi Kualitas Minyak Goreng Dengan Parameter Viskositas Dan Densitas Mengggunakan Metode Fuzzy Logic

2021 ◽  
Vol 11 (1) ◽  
pp. 22-28
Author(s):  
Alex Surapati ◽  
Azam Zyaputra ◽  
Reza Satria Rinaldi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Oil Quality ◽  
Test Results ◽  
Inference System ◽  
The Public ◽  
Cooking Oil ◽  
Market Needs

AbstrakThe quality of cooking oil sold in the market needs to be checked to ensure its health. cooking oil quality detector is designed to make it easier for the public to know the quality of the cooking oil. The research method is to make tools and conduct testing. The test is carried out by measuring the viscosity and density using the tool made. When the viscosity of 985 fuzzification was "good", and the density was 542.93 Kg/mL of "good" fuzzification, the fuzzification was processed by a fuzzy inference system, then defuzzification occurred in the form of oil quality results. fried "good". When the viscosity of 932 fuzzification is "sufficient", and the density is 618.69 Kg/mL of "moderate" fuzzification, a fuzzy inference system occurs, a defuzzification process is "moderate", when the viscosity of 926 fuzzification is "bad", and a density of 631.31 Kg/mL fuzzification "bad", fuzzy inference system occurs, defuzzification process occurs with "bad" results. To ensure that the results are accurate, the sample is taken to the BPOM which measures free fatty acids. From the BPOM test results converted to viscosity and density. In order to obtain an accurate conversion value between viscosity and density, it is recommended that a large number of samples be tested..Keywords: viscosity, density, fuzzy logic

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