Computing Transition Probability in Markov Chain for Early Prediction of Software Reliability

Early prediction of software reliability provides basis for evaluating potential reliability during early stages of a project. It also assists in evaluating the feasibility of proposed reliability requirements and provides a rational basis for design and allocation decisions. Many researchers have proposed different approaches to predict the software reliability based on a Markov model. The transition probabilities in between the states of the Markov model are input parameters to predict the software reliability. In the existing approaches, these probabilities are either assumed on some knowledge or computed using analytical method, and hence, it does not give accurate predicted reliability figure. Some authors compute them using operational profile data, but that is possible only after the deployment of the software, and this is not early prediction. The work in this paper is devoted to demonstrate the computation of transition probability in the Markov reliability model taking a case study. The proposed approach has been validated on 47 sets of real data.