基于Markov决策过程用交叉熵方法优化软件测试

研究了待测软件某些参数已知的条件下,以最小化平均测试费用为目标的软件测试优化问题.将软件测试过程处理成马尔可夫(Markov)决策过程,给出了软件测试的马尔可夫决策模型,运用交叉熵方法,通过一种学习策略获得软件测试的最优测试剖面,用于优化软件测试.模拟结果表明,学习策略给出的测试剖面要优于随机测试策略,检测和排除相同数目的软件缺陷,学习策略比随机测试能够显著地减少测试用例数,降低测试成本,提高缺陷检测效率.

Cross-Entropy Method Based on Markov Decision Process for Optimal Software Testing

This paper demonstrates an approach to optimize software testing by minimizing the expected cost with given software parameters of concern. Taking software testing process as a Markov decision process, a Markov decision model of software testing is proposed in this paper, and by using a learning strategy based on the cross-entropy method to optimize the software testing, this paper obtains the optimal testing profile. Simulation results show that the testing profile with the learning strategy performs significantly better than the random testing strategy with respect to the expected cost. Moreover, this learning strategy is more feasible and can significantly reduce the number of test cases required to detect and remove a certain number of software defects in comparison with the random testing strategy.