Software reliability estimation method based on Markov usage models using Importance Sampling

Importance sampling (IS) is a change-of-measure technique for speeding up the simulation of rare events in stochastic systems. In this paper, we presented an approach uses Importance Sampling technique for efficient estimation of software reliability via Markov software usage models in statistical testing. By suitable changes of the probabilities of state transitions during test, an iterative method based on the Ali-Silvey distance is proposed for this choice, and an unbiased reliability estimator with zero variance is obtained. A learning algorithm for the computation of optimal transition probabilities of the Markov chain usage model is also presented and experimental results of this algorithm are reported.