中文摘要：

自适应随机测试方法将测试用例均匀分布于整个输入空间，以提高测试效率．然而，当被测软件的输入参数存在错误相关性，使失效区域形状成为“片状”时，传统的自适应随机测试方法效率将急剧下降．针对“片状”失效区域的特点，本文提出了一种新的自适应随机测试方法：组合自适应随机测试方法．该方法将输入参数划分为多个不同的组；每一组被视作一个独立的输入空间并通过自适应随机测试方法生成“准”测试用例序列；最终的测试用例将由各组“准”测试用例组合而成．实验表明，组合自适应随机测试方法能大幅度提高测试用例发现错误的效率．

英文摘要：

Adaptive random testing （ ART } can improve testing effectiveness over random testing by evenly spreading test cases all over input space. However, traditional ART algorisms do not favor with chop failure pattern that is caused by fault interaction between parameters. In this paper, a new approach is proposed, which is named as combinatorial adaptive random testing （CART）. At first, CART divides parameter set into several groups according to interaction relationships of inputs. Each group is treated as a separate in- put space. Semi test case serials are generated for each parameter group according to ART algorisms. New test case is generated by combining semi test cases. Simulation results show that CART will be much more effective than traditional ART methods.