Statistics and stochastic-process theories, along with the mathematical modelling and the respective empirical evidence support, describe the software fault-debugging phenomenon. In software-reliability engineering literature, stochastic mathematical models based on the non-homogeneous Poisson process (NHPP) are employed to measure and boost reliability too. Since reliability evolves on account of the running of computer test-run, NHPP type of discrete time-space models, or difference-equation, is superior to their continuous time-space counterparts. The majority of these models assume either a constant, monotonically increasing, or decreasing fault-debugging rate under an imperfect fault-debugging environment. However, in the most debugging scenario, a sudden change may occur to the fault-debugging rate due to an addition to, deletion from, or modification of the source code. Thus, the fault-debugging rate may not always be smooth and is subject to change at some point in time called changepoint. Significantly few studies have addressed the problem of change-point in discrete-time modelling approach. The paper examines the combined effects of change-point and imperfect fault-debugging with the learning process on software-reliability growth phenomena based on the NHPP type of discrete timespace modelling approach. The performance of the proposed modelling approach is compared with other existing approaches on an actual software-reliability dataset cited in literature. The findings reveal that incorporating the effect of change-point in software-reliability growth modelling enhances the accuracy of software-reliability assessment because the stochastic characteristics of the software fault-debugging phenomenon alter at the change-point.

Nonhomogeneous Poisson process based software reliability models play an important role in developing software systems and enhancing the performance of computer software. As software reliability grows on the basis of the execution of computer test runs. Nonhomogeneous Poisson process type of discrete-time software reliability models, or difference equations, is more realistic and often provides better fit than their continuous-time counterparts. Since discrete-time model conserves the properties of the continuous-time model, the estimation of its parameter would be simpler and more accurate. In this paper, we explore the importance of testing resource and imperfect debugging phenomenon consideration in software reliability growth modeling. The resultant model is very useful for the reliability analysis as the measure of reliability is computed considering the distribution of testingeffort, influence of the testing efficiency and the changes of the testing process. Using the resultant model, testing-effort control, change-point concept and optimal release policy have also been investigated. Therefore, this paper thus provides a new insight into development of discrete-time modelling in software reliability engineering, that could be of immense help to the software project manager in monitoring and controlling the testing process closely and effectively allocating the resources in order to reduce the testing cost and to meet the given reliability requirements.