基于DBSCAN算法的测试用例优化方法

Test Case Optimization Method Based on DBSCAN Algorithm

在软件测试研究领域,测试用例约简一直以来都是研究的重点,目前的一些研究利用测试需求之间复杂的相互关系得到约简的测试需求集,在此基础上可以优化对应的测试用例集,但单个测试需求所对应的测试用例集可能是一个密度分布且数量较大的集合.对单个测试需求所对应的测试用例集合进行合理优化约简,本文在这个方面做了深入的研究和探索,提出了两种基于黑盒测试的类等价划分和类边界值分析策略.基于DBSCAN算法提出了科学合理的参数取值方法,提高了算法的适应问题程度和效率,结合优化的算法和两种策略从而得到优化约简的测试用例集.

In software testing field,test case reduction has been a research hotspot for a long time.Some researches currently use the complex relationship between test requirements for the test suites of test case,which can optimize the corresponding test suites on this basis.But the corresponding test case of a single test requirement may be a collection of density distribution in large quantities.This paper does an in-depth research and exploration on how to rationally optimize test case for the corresponding test suits of a single test requirement in the premise of test case.It proposes two classes based on black box testing equivalence partitioning and boundary value analysis strategy.Based on DBSCAN algorithm,it proposes a scientific and reasonable parameter selection method,and improves the adaptation degree and efficiency of algorithm.Combined with optimization algorithm and two strategies,it gets the optimal reduction set of test cases.