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The Power Law Process, also known as Non-Homogeneous Poisson Process, hasbeen used in various aspects, one of which is the software reliabilityassessment. Specifically, by using its intensity function to compute the rateof change of a software reliability as time-varying function. Justification ofBayesian analysis applicability to the Power Law Process was shown using realdata. The probability distribution that best characterizes the behavior of thekey parameter of the intensity function was first identified, then thelikelihood-based Bayesian reliability estimate of the Power Law Process underthe Higgins-Tsokos loss function was obtained. As a result of a simulationstudy and using real data, the Bayesian estimate shows an outstandingperformance compared to the maximum likelihood estimate using different samplesizes. In addition, a sensitivity analysis was performed, resulting in theBayesian estimate being sensitive to the prior selection; whether parametric ornon-parametric.

Bayesian Reliability Analysis of the Power Law Process with Respect to the Higgins-Tsokos Loss Function for Modeling Software Failure Times