A Distance-Based Dynamic Random Testing with Test Case Clustering

One goal of software testing strategies is to detect faults faster. Dynamic Random Testing (DRT) strategy uses the testing results to guide the selection of test cases, which have shown to be effective in the fault detection process. However, the effectiveness of DRT still can be improved. In this p...

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Bibliographic Details
Published in2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS) pp. 46 - 53
Main Authors Pei, Hanyu, Yin, Beibei, Cai, Kai-Yuan, Xie, Min
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
Clustering methods
dynamic random testing
Fault detection
k-means clustering
Pattern classification
Pattern clustering
software cybernetics
Software testing
test case distance
testing profile
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Summary:One goal of software testing strategies is to detect faults faster. Dynamic Random Testing (DRT) strategy uses the testing results to guide the selection of test cases, which have shown to be effective in the fault detection process. However, the effectiveness of DRT still can be improved. In this paper, a distance-based DRT (D-DRT) strategy is proposed. The vectorized test cases are partitioned with k-means clustering method to obtain better classification, and the distance information are used to guide the test case selection, then the test cases that are close to failure-causing test cases are more likely to be selected, thus the testing process can be optimized. In the case study, the performance of D-DRT and other testing strategies are compared. The experiment results show that the proposed D-DRT strategy has better fault detection effectiveness than the others without significant increase in computational cost.
DOI:10.1109/QRS.2019.00019