Evaluating Testing Methods by Delivered Reliability
IEEE Transactions on Software Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Modular Operational Test Plans for Inferences on Software Reliability Based on a Markov Model
IEEE Transactions on Software Engineering
Optimal testing-resource allocation with genetic algorithm for modular software systems
Journal of Systems and Software
Resource allocation during tests for optimally reliable software
Computers and Operations Research
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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.