Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion
Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion
复制标题
利用最大的可用区域：通过排除进行自适应随机测试的主动方法
DOI：
10.1109/access.2020.2977777
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Rubing Huang
中科院分区：
文献类型：
--
作者：
Jinfu Chen;Qihao Bao;T. H. Tse;Tsong Yueh Chen;Jiaxiang Xi;Chengying Mao;Minjie Yu;Rubing Huang
自适应随机测试（ART）通过使测试用例分布更均匀来提高随机测试的有效性。特别地，受限随机测试（RRT）是一种ART算法，其基于跳过先前执行的测试用例的邻域（或区域）内的所有候选测试用例的直觉。RRT在故障检测方面比RT具有更高的有效性，但会导致更高的时间成本。在本文中，我们的目标是进一步减少RRT的时间成本，提高RT和ART方法的有效性。我们提出了一个积极的技术称为“RRT的最大可用区”（RRT-LAZ）。像RRT一样，RRT-LAZ首先在每个执行的测试用例周围定义一个排除区，以确定可用的区域。与原始RRT不同，RRT-LAZ然后比较所有可用区域以主动选择最大的区域，从中随机生成下一个测试用例。仿真分析和实证研究已被用来调查的效率和有效性的RT-LAZ的RT和相关的ART算法。结果表明，RRT-LAZ具有显着低于RRT的时间成本。此外，RRT-LAZ是更有效的比RT和相关的ART方法块故障模式在低维输入空间。一般来说，由于RRT-LAZ在生成下一个病例时采用主动技术而不是被动技术，因此它比RRT更具成本效益。RT-LAZ也比RT和我们研究过的其他ART方法更具成本效益。
Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as “RRT by largest available zone” (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied.