Robustness of Non-homogeneous Gamma Process-Based Software Reliability Models

Abstract

In this paper we extend non-homogeneous gamma process (NHGP)-based software reliability models (SRMs) by Ishii and Dohi (2008) from both view points of modeling and parameter estimation. In modeling, we generalize the underlying NHGP-based SRMs to those for eleven kinds of trend function, which can characterize a variety of software fault-detection patterns. In parameter estimation, we develop a non-parametric maximum likelihood estimation method without the complete knowledge on trend functions, and compare it with the parametric maximum likelihood estimation method. Since an NHGP involves a nonhomogeneous Poisson processes (NHPPs) as the simplest case, it is shown that NHGP-based SRMs are much more robust than the common NHPP-based SRMs and that our non-parametric method can improve the goodness-of-fit performance of the conventional parametric one.