基于向量相似度的测试用例集约简方法  

Test Case Reduction Method Based on Vector Similarity Algorithm

作　　者：刘锋[1] 李朋[1] 朱二周[1] LIU Feng LI Peng ZHU Er-zhou(School of Computer Science and Technology, Anhui University, Hefei 230601, China)

机构地区：[1]安徽大学计算机科学与技术学院,安徽合肥230601

出　　处：《微电子学与计算机》2017年第3期35-39,共5页Microelectronics & Computer

基　　金：国家自然科学基金(61300169)

摘　　要：测试用例集约简的目的是在保证原有测试用例集覆盖度不变的情况下,使用一定的方法策略,尽可能的缩减测试用例集的数量,从而达到提高软件测试效率、降低测试成本的目标.本文的向量相似度算法是先利用二元向量的相似性函数,然后计算出测试用例之间的相似度、覆盖度和冗余度,最后根据计算结果选择最佳测试用例,从而得到约简后的最优或近似最优测试用例集.通过实验表明,该算法与现有的GRE算法相比:约简后测试用例集的数量平均降低约10%,而测试用例集总的冗余度平均降低约13%.The purpose of test suit reduction is to improve the efficiency of software testing and reduce the cost of test. At the same time, the coverage of the original test cases should be ensured invariable and the number of test cases could be reduced as possible with the use of a certain strategy. Vector similarity algorithm is that the binary vector similarity function should be used firstly, then the similarity, coverage and redundancy test cases needed be calculated, and the best test cases is selected according to the calculation result, finally we will get the optimal or approximate optimal test cases after the reduction. Compared with the existing algorithm of GRE, experiments show that the number of test cases decreased by about 10% averagely and the whole redundancy rate of test cases decreased by about 13% averagely according to the vector similarity algorithm.

关 键 词：测试用例集约简 软件测试 向量相似度 二元向量 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]