Open AccessEmpirical analysis of software quality prediction using a TRAINBFG algorithm
Author(s) -
Saumendra Pattnaik,
Binod Kumar Pattanayak
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.6.10780
Subject(s) - computer science , matlab , artificial neural network , software , data mining , software quality , algorithm , set (abstract data type) , fuzzy logic , machine learning , quality (philosophy) , artificial intelligence , software development , operating system , philosophy , epistemology , programming language
Software quality plays a major role in software fault proneness. That’s why prediction of software quality is essential for measuring the anticipated faults present in the software. In this paper we have proposed a Neuro-Fuzzy model for prediction of probable values for a predefined set of software characteristics by virtue of using a rule base. In course of it, we have used several training algorithms among which TRAINBFG algorithm is observed to be the best one for the purpose. There are various training algorithm available in MATLAB for training the neural network input data set. The prediction using fuzzy logic and neural network provides better result in comparison with only neural network. We find out from our implementation that TRAINBFG algorithm can provide better predicted value as compared to other algorithm in MATLAB. We have validated this result using the tools like SPSS and MATLAB.