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Assessing Pareto Type II Software Reliability using SPC

by R. Satya Prasad, G. Sridevi, K. Sita Kumari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 3
Year of Publication: 2013
Authors: R. Satya Prasad, G. Sridevi, K. Sita Kumari
10.5120/10060-4652

R. Satya Prasad, G. Sridevi, K. Sita Kumari . Assessing Pareto Type II Software Reliability using SPC. International Journal of Computer Applications. 62, 3 ( January 2013), 17-21. DOI=10.5120/10060-4652

@article{ 10.5120/10060-4652,
author = { R. Satya Prasad, G. Sridevi, K. Sita Kumari },
title = { Assessing Pareto Type II Software Reliability using SPC },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 3 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number3/10060-4652/ },
doi = { 10.5120/10060-4652 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:41.177457+05:30
%A R. Satya Prasad
%A G. Sridevi
%A K. Sita Kumari
%T Assessing Pareto Type II Software Reliability using SPC
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 3
%P 17-21
%D 2013
%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 software life cycle are essential for producing and maintaining quality/reliable software systems. It can also be monitored efficiently using Statistical Process Control (SPC). It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of Interval domain data using mean value function of Pareto type II distribution, which is based on Non-Homogenous Poisson Process (NHPP). The maximum likelihood estimation approach is used to estimate the unknown parameters of the model. We also present an analysis of failure data sets at a particular point.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability NHPP Pareto type II distribution Parameter Estimation Interval Domain data ML Estimation Statistical Process Control Mean value function Control charts

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