A three-parameter fault-detection software reliability model with the uncertainty of operating environments

As requirements for system quality have increased, the need for high system reliability is also increasing. Software systems are extremely important, in terms of enhanced reliability and stability, for providing high quality services to customers. However, because of the complexity of software systems, software development can be time-consuming and expensive. Many statistical models have been developed in the past years to estimate software reliability. In this paper, we propose a new three-parameter fault-detection software reliability model with the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness-of-fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models based on three sets of failure data collected from software applications. The results show that the proposed model fits significantly better than other existing NHPP models based on three criteria such as mean squared error (MSE), predictive ratio risk (PRR), and predictive power (PP).