Nonhomogeneous Markov Process Modeling for Software Reliability Assessment

In this article, we focus on nonhomogeneous Markov processes (NHMPs), which are generalizations of the well-known homogeneous Markov processes (HMPs) and nonhomogeneous Poisson processes, and compare two software reliability models (SRMs) which can be classified into a generalized binomial process (GBP) and a generalized Polya process (GPP). GBP and GPP are also characterized, respectively, as a Markov inverse death process and a Markov birth process, with state- and time-dependent transition rates. We develop a unified software reliability modeling framework based on the NHMPs and apply it to the software reliability prediction. Through numerical examples with the fault count data observed in actual closed-source software (CSS) and open-source software (OSS) development projects, we compare two SRMs (GBP and GPP) in terms of the goodness-of-fit and predictive performances, in addition to the quantitative software reliability assessment. We also consider software release problems with these generalized SRMs, and investigate the impact on the software release decision.