Adaptive random testing (ART) achieves better failure-detection effectiveness than random testing due to its even spreading of test cases. ART by random partitioning (RP-ART) is a lightweight method, but its advantage over random testing is relatively low. Although iterative partition testing (IPT) method has good performance for detecting failures in a block pattern, it loses randomness during the test case generation. To overcome the shortcomings of the above two algorithms, a new algorithm named ART by flexible partitioning (FP-ART) is proposed. In the FP-ART, a set of random candidates is used to select an appropriate test case by considering their boundary distance. Accordingly, the corresponding sub-domain is also partitioned by the new test case. Based on this kind of flexible partitioning, the randomness of test case selection can be guaranteed and the spatial distribution of test cases is even more diverse. According to the results in simulation and empirical experiments, FP-ART demonstrates better failure-detection effectiveness than RP-ART and is more suitable to detect the failures in strip patterns than the IPT method. Meanwhile, its failure-detection ability is much stronger than that of fixed-size-candidate-set ART in the cases of a relatively high failure rate.

中文翻译：

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基于灵活分区的自适应随机测试

自适应随机测试（ART）由于其测试案例分布均匀，因此比随机测试具有更好的故障检测效率。通过随机分区（RP-ART）进行ART是一种轻量级的方法，但其相对于随机测试的优势相对较低。尽管迭代分区测试（IPT）方法在检测块模式中的故障方面具有良好的性能，但在测试用例生成过程中会失去随机性。为了克服上述两种算法的缺点，提出了一种新的名为柔性划分的算法（FP-ART）。在FP-ART中，通过考虑候选边界的距离，使用一组随机候选来选择适当的测试用例。因此，相应的子域也被新的测试用例划分。基于这种灵活的分区，可以保证测试用例选择的随机性，并且测试用例的空间分布更加多样化。根据仿真和经验实验的结果，FP-ART比RP-ART表现出更好的故障检测效率，比IPT方法更适合检测带钢图案的故障。同时，在故障率相对较高的情况下，它的故障检测能力比固定尺寸的候选ART技术要强得多。