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Software Reliability Prediction using Neural Network with Encoded Input

by Manjubala Bisi, Neeraj Kumar Goyal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 22
Year of Publication: 2012
Authors: Manjubala Bisi, Neeraj Kumar Goyal
10.5120/7492-0586

Manjubala Bisi, Neeraj Kumar Goyal . Software Reliability Prediction using Neural Network with Encoded Input. International Journal of Computer Applications. 47, 22 ( June 2012), 46-52. DOI=10.5120/7492-0586

@article{ 10.5120/7492-0586,
author = { Manjubala Bisi, Neeraj Kumar Goyal },
title = { Software Reliability Prediction using Neural Network with Encoded Input },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 22 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number22/7492-0586/ },
doi = { 10.5120/7492-0586 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:34.924386+05:30
%A Manjubala Bisi
%A Neeraj Kumar Goyal
%T Software Reliability Prediction using Neural Network with Encoded Input
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 22
%P 46-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Failure Prediction Neural Network Encoded Input Encoded Parameter.

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