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A Markov Decision Approach to Optimize Testing Profile in Software Testing

机译:在软件测试中优化测试配置文件的马尔可夫决策方法

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In this paper, we demonstrate an approach to optimize software testing, minimize 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, and by using a learning strategy based on the Cross-Entropy method to optimize the software testing, we obtain the optimal testing profile. Simulation results show that this learning strategy reduces significantly in expected cost comparing with random testing, moreover, this learning strategy is more feasible and significantly in reducing the number of test cases required to detect and revealing a certain number of software defects than random testing.
机译:在本文中,我们演示了一种优化软件测试的方法,可以在给定的软件参数关注的情况下最大程度地降低预期成本。以软件测试过程作为马尔可夫决策过程,提出了软件测试的马尔可夫决策模型,并采用基于交叉熵方法的学习策略对软件测试进行了优化,得到了最优的测试轮廓。仿真结果表明,与随机测试相比,该学习策略显着降低了预期成本,并且与随机测试相比,该学习策略更加可行,并且在减少检测和揭示一定数量的软件缺陷所需的测试用例数量方面更为显着。

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