A Stochastic Software Reliability Growth Model with Learning and Change-point

Over the past years, many software reliability growth models (SRGMs) have been proposed and these models treated the software fault detection process as a discrete state space. However, as the size of software system becomes larger, the SRGMs based on a continuous state space were proposed. Although some researchers have investigated the continuous state space SRGMs, none of them considered learning phenomenon and the problem of change-point in the fault detection process. In actually, the fault detection process is complex and can be affected by many different factors. The fault detection rate may be a change if the testing environment, strategy or resource allocation is changed. Thus, in order to describe the realistic situations, a continuous state space SRGM based on Ito type Stochastic Differential Equation (SDE) SRGMs with learning and change-point have been proposed. Further, actual software reliability data have been used to demonstrate the proposed model.