A stochastic formulation of successive software releases with faults severity

Software companies are coming with multiple add-ons to survive in the pure competitive environment. Each succeeding up-gradation offers some performance enhancement and distinguishing itself from the past release. If the size of the software system is large, the number of faults detected during the testing phase becomes large, and the number of faults, which are removed through each debugging, becomes small compared to initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a multi-release software reliability growth model based on Itô´s type of differential equation. The model categorizes Faults in two categories: simple and hard with respect to time which they take for isolation and removal after their observation. The model developed is validated on real data set.