A learning strategy for software testing optimization based on dynamic programming

The optimization of software testing is one of the essential problems. In this paper, a stochastic Markov Decision Process (MDP) model of software testing is proposed, and the process of software testing is described as a reinforcement learning problem. A learning strategy based on the policy iteration of dynamic programming is presented to obtain the optimal testing profile. The case study indicates that, compared with random testing strategy, our learning strategy can significantly reduce the testing cost to detect and remove a certain number of software defects.