A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information

Abstract

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.