TITLE:
One-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability
AUTHORS:
Albert Orwa Akuno, Luke Akong’o Orawo, Ali Salim Islam
KEYWORDS:
Nonhomogeneous Poisson Process, Non-Informative Priors, Software Reliability Models, Bayesian Approach
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.5,
August
25,
2014
ABSTRACT: The Goel-Okumoto software reliability model, also
known as the Exponential
Nonhomogeneous Poisson Process,is one of
the earliest software reliability models to be proposed. From literature, it is
evident that most of the study that has been done on the Goel-Okumoto software reliability model is
parameter estimation using the MLE method and model fit. It is widely known
that predictive analysis is very useful for modifying, debugging and
determining when to terminate software development testing process. However,
there is a conspicuous absence of literature on both the classical and Bayesian
predictive analyses on the model. This paper presents some results about
predictive analyses for the Goel-Okumoto software reliability model. Driven by
the requirement of highly reliable software used in computers embedded in
automotive, mechanical and safety control systems, industrial and quality
process control, real-time sensor networks, aircrafts, nuclear reactors among
others, we address four issues in single-sample prediction associated closely
with software development process. We have adopted Bayesian methods based on
non-informative priors to develop explicit solutions to these problems. An
example with real data in the form of time
between software failures will be used to illustrate the developed
methodologies.