一种针对高维输入域的适应性随机测试改进性算法  

An improved adaptive random testing method in high dimensional input domains

作　　者：占徐政 ZHAN Xu-zheng(School of Software and Internet of Thing Engineering,Jiangxi University of Finance and Economics,Nanchang 330032,China)

机构地区：[1]江西财经大学软件与物联网工程学院,江西南昌330032

出　　处：《计算机工程与科学》2018年第11期1936-1943,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61762040);江西省自然科学基金重点项目(20171ACB21031)

摘　　要：适应性随机测试ART能够保证测试用例在输入域中更加均匀地分布,从而在失效检测能力上明显强于基本的随机测试,其中,固定候选集规模的ART算法FSCS-ART因具备较好的揭错能力而被广泛采用。然而随着输入域维度的升高,FSCS-ART的失效检测效果显著降低。针对该问题,在从候选集中选择正式用例时综合考虑两种距离:候选点与已测用例之间的距离和它与中心点之间的距离,这样,输入域边缘的候选点的优先级得以降低,有效地克服了FSCS-ART趋向于边缘的弊端。实验结果表明,改进后的算法针对高维输入域表现出更强的失效检测能力。Adaptive random testing （ART） ensures that test cases are more evenly distributed in the input domain, and thus achieves significantly stronger failure detection capability than the basic random testing. Among the existing ART methods, the fixed size candidate set ART （FSCS ART） exhibits better failure detection capability and has extensive applications. However, its failure detection effec tiveness deteriorates significantly with the increase of input domain dimensions. To solve this high di mension problem, two types of distances are taken into account while choosing a test case from the can didate set： one is the distance from each candidate point to the already executed test cases; the other is the distance from individual candidate point to the center point. The comprehensive consideration of dis tances can reduce the priority of the candidate points at the edge of the input domain and overcome the disadvantage of the FSCS ART. Experimental results show that the improved algorithm achieves a stronger failure detection capability in high dimensional input domains.

关 键 词：软件测试 适应性随机测试 测试用例 失效检测能力 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]