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Burr Type XII Software Reliability Growth Model

by R.satya Prasad, K.v.murali Mohan, G.sridevi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 16
Year of Publication: 2014
Authors: R.satya Prasad, K.v.murali Mohan, G.sridevi
10.5120/18995-0452

R.satya Prasad, K.v.murali Mohan, G.sridevi . Burr Type XII Software Reliability Growth Model. International Journal of Computer Applications. 108, 16 ( December 2014), 16-20. DOI=10.5120/18995-0452

@article{ 10.5120/18995-0452,
author = { R.satya Prasad, K.v.murali Mohan, G.sridevi },
title = { Burr Type XII Software Reliability Growth Model },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 16 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 16-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number16/18995-0452/ },
doi = { 10.5120/18995-0452 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:43:08.731227+05:30
%A R.satya Prasad
%A K.v.murali Mohan
%A G.sridevi
%T Burr Type XII Software Reliability Growth Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 16
%P 16-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software Reliability Growth model (SRGM) is a mathematical model of how the software reliability improves as faults are detected and repaired. The development of many SRGMs over the last several decades have resulted in the improvement of software facilitating many engineers and managers in tracking and measuring the growth of reliability. This paper proposes Burr type XII based Software Reliability growth model with time domain data. The unknown parameters of the model are estimated using the maximum likelihood (ML) estimation method. Reliability of a software system using Burr type XII distribution, which is based on Non-Homogenous Poisson process (NHPP), is presented through estimation procedures. The performance of the SRGM is judged by its ability to fit the software failure data. How good does a mathematical model fit to the data is also being calculated. To access the performance of the considered SRGM, we have carried out the parameter estimation on the real software failure datasets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability Burr type XII distribution NHPP ML Estimation

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