A stochastic combinatorial optimization model for test sequence optimization

Traditional test sequence optimization methods for path testing have three problems: 1) fake test results may occur; 2) unnecessary repetitive tests may exist; 2) actual test coverage rate could be low. These problems are because the effects of the faults on the test sequence execution are not considered by traditional methods. To solve these problems, we defined a stochastic combinatorial optimization model in this paper. At the same time, we constructed a multistage dynamic combinatorial optimization model to solve it. In each stage, the stochastic optimization is transferred into a deterministic optimization and the faults are taken as the stage changing factors. Simulation results show that the effective test efficiency and test coverage rate are evidently increased by this stochastic combinatorial optimization model.