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An Approach to Generating Test Data for EFSM Paths Considering Condition Coverage

机译:考虑条件覆盖范围的EFSM路径测试数据生成方法

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Model-based test case generation has become a hotspot, and automatic generation of test data is difficult in this area. In this paper, system model is represented by extended finite state machine(EFSM), and genetic algorithm is used to generate test data for EFSM paths. When computing the fitness of an individual, the branch distance and the ratio of uncovered conditions of the individual are considered. In experiments, the proposed method is compared with the Kalaji's, and the results show that our method has a better effect and can get higher quality test data.
机译:基于模型的测试用例的生成已成为一个热点,在这一领域很难自动生成测试数据。本文以扩展有限状态机(EFSM)为代表的系统模型,并采用遗传算法生成EFSM路径的测试数据。在计算一个人的适应度时,应考虑其分支距离和该人未发现状况的比率。在实验中,将该方法与Kalaji方法进行了比较,结果表明我们的方法效果更好,可以获得更高质量的测试数据。

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