Using Program Slicing to Improve the Efficiency and Effectiveness of Cluster Test Selection
Using Program Slicing to Improve the Efficiency and Effectiveness of Cluster Test Selection
复制标题
DOI：
10.1142/s0218194011005487
复制
发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
Zhenyu Chen;Y. Duan;Zhihong Zhao;Baowen Xu;Ju Qian
中科院分区：
文献类型：
--
作者：
Zhenyu Chen;Y. Duan;Zhihong Zhao;Baowen Xu;Ju Qian
聚类测试选择是一种新的回归测试子集选择方法。为了提高聚类测试选择技术的效率和有效性，引入了程序切片技术。在修改后的代码上计算静态切片。每个测试用例的执行剖面被程序切片过滤，以突出受修改影响的软件部分，称为切片过滤。切片过滤降低了聚类分析的数据维数，从而大大节省了聚类测试选择的成本。实验结果表明，切片过滤技术可以显著降低聚类测试选择的代价，并在一定程度上提高聚类测试选择的有效性。因此，通过过滤进行聚类测试选择具有更大的潜在可扩展性，以应对大型软件。
Cluster test selection is a new successful approach to select a subset of the existing test suite in regression testing. In this paper, program slicing is introduced to improve the efficiency and effectiveness of cluster test selection techniques. A static slice is computed on the modified code. The execution profile of each test case is filtered by the program slice to highlight the parts of software affected by modification, called slice filtering. The slice filtering reduces the data dimensions for cluster analysis, such that the cost of cluster test selection is saved dramatically. The experiment results show that the slice filtering techniques could reduce the cost of cluster test selection significantly and could also improve the effectiveness of cluster test selection modestly. Therefore, cluster test selection by filtering has more potential scalability to deal with large software.