Stochastic Differential Equation Based Software Reliability Growth Modeling With Change Point and Two Types of Imperfect Debugging

Software Reliability is defined as the probability of failure free operation for a specified period of time in a specified environment. If the size of the software system is large, and the number of the faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial faults content at the beginning of the testing phase, in such a situation, we can model the software faults detection process as a stochastic process with continuous state space. Due to the complexity of large software system and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/detection of a failure and the original fault may remain resulting in a phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. During software testing fault detection/correction rate may not be same throughout the whole testing process, but it may change at any time moment known as change-point. In this paper, we have proposed o it ˆ type of stochastic differential equation (SDE) based Software Reliability Growth Models (SRGM) with change-point and two types of imperfect debugging. The proposed model is validated on number of data sets and compared with the result of other established models.