TITLE:
One-Sample Bayesian Predictive Analyses for a Nonhomogeneous Poisson Process with Delayed S-Shaped Intensity Function Using Non-Informative Priors
AUTHORS:
Otieno Collins, Orawo Luke Akong’o, Matiri George Munene
KEYWORDS:
Failure Intensity, Non-Informative Priors, Software Reliability Model, Bayesian Approach, Non-Homogeneous Poisson Process
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.5,
October
10,
2023
ABSTRACT: 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.