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Srinivas Sethi
Computer Science Engineering and Application, IGIT, Sarang, India
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Software Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach Manmath Kumar Bhuyan; Durga Prasad Mohapatra; Srinivas Sethi
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.451 KB) | DOI: 10.11591/ijece.v6i4.pp1929-1938

Abstract

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 recurrentneural 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.
Co-Authors Durga Prasad Mohapatra Manmath Kumar Bhuyan