使用Voronoi图进行高效和有效的随机测试

T. Chen, Robert G. Merkel
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引用次数: 22

摘要

自适应随机测试(ART)是一种提高随机测试故障发现效率的方法。固定大小的候选集ART是该方法研究最多的变体。然而,现有的FSCS-ART实现有大量的选择开销,n个测试用例需要O(n/sup 2/)时间来生成。我们描述了一种称为Voronoi图的几何数据结构的使用,以将此开销降低到不低于O(n/spl radial /n),并且经过进一步优化,可以降低到O(nlogn)。我们通过实验证明,使用这种改进的实现可以获得选择开销方面的实际改进。
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Efficient and effective random testing using the Voronoi diagram
Adaptive random testing (ART) is a method for improving the fault-finding effectiveness of random testing. Fixed-size candidate set ART is the most studied variant of this approach. However, existing implementations of FSCS-ART have had substantial selection overhead, with n test cases requiring O(n/sup 2/) time to generate. We describe the use of a geometric data structure known as the Voronoi diagram to reduce this overhead to no worse than O(n/spl radic/n) and, with further optimization, O(nlogn). We demonstrate experimentally that practical improvements in selection overhead can be gained using this improved implementation.
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