Stochastic software reliability growth modelling with fault introduction and change point

The rapid utilization of computer-based automated systems for human tasks has caused a significant shift in society. Today's society places a high value on software stability. To create highly reliable software systems, software testing is required. Software reliability growth models estimate the number of software faults during the testing stage in order to assess software reliability. The fault removal process is usually assumed to be deterministic, but as software systems grow larger and more faults are discovered during testing phase. The number of faults that are discovered and removed during each debugging process decreases until it is insignificant compared to the fault content at the beginning of the testing phase. It is very likely that the method used in this scenario to find software faults is a stochastic process with a continuous state space. Dynamic indeterministic fluctuations such as testing efficiency, testing method, testing effort expenditure, and testing strategy. invariably have an impact on testing progress and incorporates the change point concept in fault detection rate. In this paper, we provide a software reliability growth model (SRGM) based on a stochastic differential equation (SDE). It also incorporates the change-point idea, which states that the rate of detection per remaining problem or fault may alter as a testing method shifts. The applicability and accuracy of the suggested model are demonstrated with software failure data sets. The model's validity was assessed using predictive validity and mean squared error. Finally, the suggested models were compared to existing continuous-state space SRGM using stochastic differential equations to determine their goodness of fit. It has been demonstrated that an SDE-based model with a change point performs significantly better than the existing model.