An Efficient Approach to Simulation Vector Generation Using Bayesian Network

Improving the efficiency of simulation-based validation is important.Most of simulation vectors for regression test are huge and unnecessary.They made the covering process inefficient.In this paper,we used Bayesian network to describe the relation between the inputs and the branch statements. The new simulation vectors were generated by reasoning on the network. We performed experiments on some functional modules.The results indicate that the average vector length generated by the Bayesian network using different reference algorithms is about 10％ of the original one,but the best path coverage even exceeds the original one.