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Software Reliability Prediction using Neural Network with Encoded Input

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 22
Year of Publication: 2012
Authors:
Manjubala Bisi
Neeraj Kumar Goyal
10.5120/7492-0586

Manjubala Bisi and Neeraj Kumar Goyal. Article: Software Reliability Prediction using Neural Network with Encoded Input. International Journal of Computer Applications 47(22):46-52, June 2012. Full text available. BibTeX

@article{key:article,
	author = {Manjubala Bisi and Neeraj Kumar Goyal},
	title = {Article: Software Reliability Prediction using Neural Network with Encoded Input},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {22},
	pages = {46-52},
	month = {June},
	note = {Full text available}
}

Abstract

A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.

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