%0 Journal Article
%T One-Sample Bayesian Predictive Analyses for a Nonhomogeneous Poisson Process with Delayed S-Shaped Intensity Function Using Non-Informative Priors
%A Otieno Collins
%A Orawo Luke AkongˇŻo
%A Matiri George Munene
%J Open Journal of Statistics
%P 717-733
%@ 2161-7198
%D 2023
%I Scientific Research Publishing
%R 10.4236/ojs.2023.135034
%X 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 </span><span style=\"font-family:Verdana;\">reliability analysis. Predictive analysis in
software testing is useful in modif</span><span style=\"font-family:Verdana;\">y</span><span style=\"font-family:Verdana;\">ing,
debugging, and determining when to terminate software development testing
processes. However, Bayesian predictive analyses on the delayed</span><span style=\"font-family:Verdana;\"> 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.
%K Failure Intensity
%K Non-Informative Priors
%K Software Reliability Model
%K Bayesian Approach
%K Non-Homogeneous Poisson Process
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=128203