  The Power Law Process, also known as Non-Homogeneous Poisson Process, has
been used in various aspects, one of which is the software reliability
assessment. Specifically, by using its intensity function to compute the rate
of change of a software reliability as time-varying function. Justification of
Bayesian analysis applicability to the Power Law Process was shown using real
data. The probability distribution that best characterizes the behavior of the
key parameter of the intensity function was first identified, then the
likelihood-based Bayesian reliability estimate of the Power Law Process under
the Higgins-Tsokos loss function was obtained. As a result of a simulation
study and using real data, the Bayesian estimate shows an outstanding
performance compared to the maximum likelihood estimate using different sample
sizes. In addition, a sensitivity analysis was performed, resulting in the
Bayesian estimate being sensitive to the prior selection; whether parametric or
non-parametric.
