Early Software Quality Prediction Based on Software Requirements Specification Using Fuzzy Inference System

2018 ◽  
pp. 722-733
Author(s):  
Muhammad Hammad Masood ◽  
Malik Jahan Khan
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Requirements Specification ◽  
Software Requirements ◽  
Quality Prediction ◽  
Inference System ◽  
Software Requirements Specification ◽  
Software Quality Prediction

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.

Method of Evaluating the Weights of Software Quality Measures and Indicators

2017 ◽  
Vol 2 (2) ◽  
pp. 16
Author(s):  
Tetiana Hovorushchenko
Keyword(s):  
Quality Assessment ◽  
Software Quality ◽  
Quality Measures ◽  
Quality Characteristics ◽  
Quality Metrics ◽  
Requirements Specification ◽  
Software Requirements ◽  
Software Requirements Specification ◽  
Software Quality Metrics ◽  
First Time

Nowadays the actual task is evaluating the mutual influences of the software quality characteristics and subcharacteristics by the measures and software quality metrics – by the indicators. The aim of this study is the development of the method of evaluating the weights of software quality measures and indicators. The first time developed method of evaluating the weights of the software quality measures and indicators differs from known methods that: considers the correlation of software quality subchcaracteristics by the measures and metrics by the indicators, calculates the weights of exactly measures and indicators, provides the conclusion about the presence of which measures and indicators in the software requirements specification (SRS) is necessary for the appropriate level of veracity of the software quality assessment. The weights of the software quality measures and indicators provide the sorting of all missing in the SRS measures and indicators in descending of their significance (in descending of their weights), i.e. the priority of their further addition in the SRS.

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.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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