SOFTWARE RELIABILITY GROWTH MODEL WITH TEMPORAL CORRELATION IN A NETWORK ENVIRONMENT

Increasingly software systems are developed to provide great flexibility to customers but also introduce great uncertainty for system development. The uncertain behavior of fault-detection rate has irregular fluctuation and is described as a Markovian stochastic processes (white noise). However, in many cases the white noise idealization is insufficient, and real fluctuations are always correlated and correlated fluctuations (or colored noise) are non-Markovian stochastic processes. We develop a new model to quantify the uncertainties within non-homogeneous Poisson process (NHPP) in the noisy environment. Based on a stochastic model of the software fault detection process, the environmental uncertainties collectively are treated as a noise of arbitrary distribution and correlation structure. Based on the stochastic model, the analytical solution can be derived. To validate our model, we consider five particular scenarios with distinct environmental uncertainty. Experimental comparisons with existing methods demonstrate that the new framework shows a closer fitting to actual data and exhibits a more accurately predictive power.