Abstract
Traditional software reliability growth models enable quantitative assessment of the software testing process by characterizing the fault detection in terms of testing time or effort. However, the majority of these models do not identify specific testing activities underlying fault discovery and thus can only provide limited guidance on how to incrementally allocate effort. Although there are several novel studies focused on covariate software reliability growth models, they are limited to model development, application, and assessment.
This paper presents a non-homogeneous Poisson process software reliability growth model incorporating covariates based on the discrete Cox proportional hazards model. An efficient and stable expectation conditional maximization algorithm is applied to identify the model parameters. An optimal test activity allocation problem is formulated to maximize fault discovery. The proposed method is illustrated through numerical examples on two data sets.
•A NHPP software reliability growth model incorporating covariates is developed.•Efficient expectation conditional maximization algorithms are derived.•The optimal testing activity allocation problem maximizes fault exposure.•Results suggest periodic application of testing activity allocation can guide test.