Software belief reliability growth model incorporating change point and imperfect debugging based on uncertain differential equation approach

Many software reliability growth models (SRGMs) have been proposed by researchers within the context of probability theory to estimate software reliability, remaining number of faults and optimal release time. The Fault Detection Rate (FDR) may vary because of changes in testing strategies. Due to lack of knowledge of software code, the testing team might be unable to rectify the detected faults thereby introducing new faults during the fault correction process. The debugging process is imperfect due to factors like human error, insufficient testing and complex codes resulting in epistemic uncertainty. In this paper, we have proposed a new software belief reliability growth model (SBRGM) using uncertain differential equations to deal with epistemic uncertainty effectively. We have incorporated imperfect debugging and change point based on the approach of belief reliability theory, making this model more accurate as compared to some of the previously developed models. Model parameters estimation methodology is derived using the least square method and Python version 3.10. Calculation of change point is done using empirical data analysis based on the First principle of Derivatives. Three real data sets have been used to validate the proposed model. This research contributes to being more flexible and realistic in dealing with epistemic uncertainty effectively as compared to conventional approaches.