%0 Journal Article
%T Bayesian Predictive Analyses for Logarithmic Non-Homogeneous Poisson Process in Software Reliability
%A Nickson Cheruiyot
%A Luke Akong¡¯o Orawo
%A Ali Salim Islam
%J Open Access Library Journal
%V 5
%N 8
%P 1-13
%@ 2333-9721
%D 2018
%I Open Access Library
%R 10.4236/oalib.1104767
%X <div style="text-align:justify;">
	<span style="line-height:1.5;">This paper discusses the Bayesian
approach to estimation and prediction of the reliability of software systems
during the testing process. A Non-Homogeneous
Poisson Process (NHPP) arising from the Musa-Okumoto (1984)
software reliability model is proposed for the software failures. The
Musa-Okumoto NHPP reliability model consists of two components¡ªthe execution
time component and the calendar time component, and is a popular model in
software reliability analysis. The predictive analyses of software reliability
model are of great importance for modifying, debugging and determining when to
terminate so</span><span style="line-height:1.5;">ftware development testing process. However, Bayesian and Classical
predictive analyses on the Musa-Okumoto
(1984) NHPP model is missing on the literature. This paper addresses four software
reliability issues in single-sample prediction associated closely with
development testing program. Bayesian approach based on non-informative prior
was adopted to develop explicit solutions to these problems. Examples based on
both real and simulated data are presented to illustrate the developed
theoretical prediction results.</span>
</div>
%K Non-Informative Priors
%K Non-Homogeneous Poisson Process
%K Bayesian  Approach
%K Intensity Function
%K Software Reliability Model
%U http://www.oalib.com/paper/5298314