A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information
A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information
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DOI：
10.1109/icst.2010.47
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
Shali Yan;Zhenyu Chen;Zhihong Zhao;Chen Zhang;Yuming Zhou
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文献类型：
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作者：
Shali Yan;Zhenyu Chen;Zhihong Zhao;Chen Zhang;Yuming Zhou
聚类过滤是一种测试选择技术，它通过减少测试规模和发现最大故障来节省测试结果检查的人力。聚类抽样策略在聚类过滤技术中起着关键作用。良好的采样策略可以极大地提高故障检测能力。本文提出了一种新的基于执行谱的聚类抽样策略(ESBS)。与现有的抽样策略不同，ESB迭代地从每个簇中选择测试用例。在每个迭代过程中，ESB选择最有可能失败的测试用例。对于每个测试，基于从同一簇中选择的先前通过和失败的测试用例的执行频谱信息来计算其可疑性。对新的采样策略进行了实验评估，结果表明，在大多数情况下，新的采样策略比现有的采样策略更有效。
Cluster filtering is a kind of test selection technique, which saves human efforts for result inspection by reducing test size and finding maximum failures. Cluster sampling strategies play a key role in the cluster filtering technique. A good sampling strategy can greatly improve the failure detection capability. In this paper, we propose a new cluster sampling strategy called execution-spectra-based sampling (ESBS). Different from the existing sampling strategies, ESBS iteratively selects test cases from each cluster. In each iteration process, ESBS selects the test case that has the maximum possibility to be a failed test. For each test, its suspiciousness is computed based on the execution spectra information of previous passed and failed test cases selected from the same cluster. The new sampling strategy ESBS is evaluated experimentally and the results show that it is more effective than existing sampling strategies in most cases.