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Software Reliability Growth Model using Interval Domain Data

by Geetha Rani Neppala, Dr.R.Satya Prasad, Prof.R.R.L.Kantam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 34 - Number 9
Year of Publication: 2011
Authors: Geetha Rani Neppala, Dr.R.Satya Prasad, Prof.R.R.L.Kantam
10.5120/4125-5941

Geetha Rani Neppala, Dr.R.Satya Prasad, Prof.R.R.L.Kantam . Software Reliability Growth Model using Interval Domain Data. International Journal of Computer Applications. 34, 9 ( November 2011), 5-9. DOI=10.5120/4125-5941

@article{ 10.5120/4125-5941,
author = { Geetha Rani Neppala, Dr.R.Satya Prasad, Prof.R.R.L.Kantam },
title = { Software Reliability Growth Model using Interval Domain Data },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 34 },
number = { 9 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume34/number9/4125-5941/ },
doi = { 10.5120/4125-5941 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:35.660451+05:30
%A Geetha Rani Neppala
%A Dr.R.Satya Prasad
%A Prof.R.R.L.Kantam
%T Software Reliability Growth Model using Interval Domain Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 9
%P 5-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software reliability is one of the most important characteristics of software quality. Its measurement and management technologies employed during the software life cycle are essential for producing and maintaining quality/reliable software systems. Over the last several decades, many Software Reliability Growth Models (SRGMs) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of reliability as software is being improved. In this paper we proposed Pareto type II based software reliability growth model with interval domain data. The maximum likelihood (ML) estimation approach is used to estimate the unknown parameters of the model. This paper presents estimation procedures to access reliability of a software system using Pareto type II distribution, which is based on Non Homogenous Poisson Process (NHPP). We also present an analysis of two software failure data sets.

References
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  10. Wood, A(1996), “Predicting Software Reliability”, IEEE Computer, 2253-2264
Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability NHPP Pareto type II distribution Parameter estimation Interval domain data ML estimation

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