Author(s)

Otieno Collins, Orawo Luke Akong’o, Matiri George Munene

Department of Mathematics, Egerton University, Egerton, Kenya.

The delayed S-shaped software reliability growth model (SRGM) is one of the non-homogeneous Poisson process (NHPP) models which have been proposed for software reliability assessment. The model is distinctive because it has a mean value function that reflects the delay in failure reporting: there is a delay between failure detection and reporting time. The model captures error detection, isolation, and removal processes, thus is appropriate for software reliability analysis. Predictive analysis in software testing is useful in modifying, debugging, and determining when to terminate software development testing processes. However, Bayesian predictive analyses on the delayed S-shaped model have not been extensively explored. This paper uses the delayed S-shaped SRGM to address four issues in one-sample prediction associated with the software development testing process. Bayesian approach based on non-informative priors was used to derive explicit solutions for the four issues, and the developed methodologies were illustrated using real data.

Failure Intensity, Non-Informative Priors, Software Reliability Model, Bayesian Approach, Non-Homogeneous Poisson Process

Collins, O. , Akong’o, O. and Munene, M. (2023) One-Sample Bayesian Predictive Analyses for a Nonhomogeneous Poisson Process with Delayed S-Shaped Intensity Function Using Non-Informative Priors. Open Journal of Statistics, 13, 717-733. doi: 10.4236/ojs.2023.135034.