Two-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.2014.49069

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Open Journal of Statistics > Vol.4 No.9, October 2014

Two-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability ()

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

Department of Mathematics, Egerton University, Egerton, Kenya.

DOI: 10.4236/ojs.2014.49069 PDF HTML XML 2,556 Downloads 3,033 Views Citations

Abstract

The Goel-Okumoto software reliability model is one of the earliest attempts to use a non-homogeneous Poisson process to model failure times observed during software test interval. The model is known as exponential NHPP model as it describes exponential software failure curve. Parameter estimation, model fit and predictive analyses based on one sample have been conducted on the Goel-Okumoto software reliability model. However, predictive analyses based on two samples have not been conducted on the model. In two-sample prediction, the parameters and characteristics of the first sample are used to analyze and to make predictions for the second sample. This helps in saving time and resources during the software development process. This paper presents some results about predictive analyses for the Goel-Okumoto software reliability model based on two samples. We have addressed three issues in two-sample prediction associated closely with software development testing process. Bayesian methods based on non-informative priors have been adopted to develop solutions to these issues. The developed methodologies have been illustrated by two sets of software failure data simulated from the Goel-Okumoto software reliability model.

Keywords

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

Share and Cite:

Akuno, A. , Orawo, L. and Islam, A. (2014) Two-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability. Open Journal of Statistics, 4, 742-750. doi: 10.4236/ojs.2014.49069.

Conflicts of Interest

The authors declare no conflicts of interest.

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