Stochastic Differential Equation-Based Testing Coverage SRGM by Using ANN Approach

Organizations must build software that is extremely dependable due to the high expense of resolving errors, safety issues, and legal obligations. Software developers have created models for measuring and tracking the evolution of dependability in their products. Most of the proposed software reliability growth models takes fault detection into account throughout both the phases of testing and the operational as counting process. In addition, the size of the software system affects how many faults are discovered during testing and also how many are discovered and rectified during debugging relative to the fault content at the beginning of the testing period. So, in a situation like this, we may conceptualize the software fault detection process in terms of testing coverage as a stochastic process with a continuous state space. In this research, we offer an Ito-type stochastic differential equation-based ANN-based testing coverage software reliability growth model. The proposed approach has been tested and examined using real failure datasets from software projects. The suggested model that incorporates the idea of stochastic differential equations in testing coverage-based SRGM outperforms the current NHPP-based model.