Open AccessSoftware Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach
Author(s) -
Manmath Kumar Bhuyan,
Durga Prasad Mohapatra,
Srinivas Sethi
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i4.pp1929-1938
Subject(s) - computer science , artificial neural network , software , fuzzy logic , reliability (semiconductor) , algorithm , machine learning , software quality , neuro fuzzy , data mining , artificial intelligence , recurrent neural network , software development , fuzzy control system , operating system , power (physics) , physics , quantum mechanics
Fuzzy Logic (FL) together with Recurrent Neural Network (RNN) is used to predict the software reliability. Fuzzy Min-Max algorithm is used to optimize the number of the kgaussian nodes in the hidden layer and delayed input neurons. The optimized recurrent neural network is used to dynamically reconfigure in real-time as actual software failure. In this work, an enhanced fuzzy min-max algorithm together with recurrent neural network based machine learning technique is explored and a comparative analysis is performed for the modeling of reliability prediction in software systems. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. The performance of our proposed approach has been tested using distributed system application failure data set.