An NHPP-based software reliability model with time-dependent fault detection rate under uncertain operating environments

In the field of software reliability, accurately modeling the fault detection process is very important for effective quality management. Existing nonhomogeneous Poisson process (NHPP)-based software reliability models (SRMs) often assume that the fault detection rate (FDR) is constant or simply decreasing, which does not sufficiently reflect the dynamic characteristics of real software testing and operating environments. In this study, we propose a new NHPP-based SRM that reflects the FDR that increases over time. The proposed model can identify a more realistic S-shaped fault detection pattern by considering the characteristics of the FDR, which is low initially and gradually increases over time. In addition, it dynamically reflects the impact of environmental change on the number of potential faults and the fault detection process by considering the uncertainty of the operating environment. In this paper, we establish and analyze the differential equations representing these relationships, and through numerical examples, we verify that the proposed model is more suitable for fault data than the existing NHPP SRMs.