Considering Multiplicative Noise in a Software Reliability Growth Model Using Stochastic Differential Equation Approach

A Software reliability growth models are very useful to investigate software reliability characteristics quantitatively and to establish relationship between the remaining faults in the software with testing time. The only way to enhance the quality and reliability of software is to detect and remove the faults during the testing phase of software. Usually, the fault removal process is assumed to be deterministic, but as software systems get bigger and more flaws are found during testing, the number of faults that are found and removed during each debugging process decreases until it is negligibly small compared to the fault content at the beginning of the testing phase. It is quite likely that software fault detection process in this scenario as a stochastic process with continuous state space. In this study, we have the concept of multiplicative noise and proposed a software reliability growth model under perfect debugging environments which is governed by stochastic differential equations. The proposed stochastic differential equation based SRGM has been validated on real-life failure data sets, and the results of the goodness of fit and comparison criteria for the proposed model exhibited the applicability of the model.