One-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability

Albert Orwa  Akuno; Luke Akong’o  Orawo; Ali Salim  Islam

doi:10.4236/ojs.2014.45039

Albert Orwa Akuno^*, Luke Akong’o Orawo, Ali Salim Islam

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.

Keywords: Nonhomogeneous Poisson Process, Non-Informative Priors, Software Reliability Models, Bayesian Approach

Cite this paper: Akuno, A. , Orawo, L. and Islam, A. (2014) One-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability. Open Journal of Statistics, 4, 402-411. doi: 10.4236/ojs.2014.45039.

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