%A Ziyuan Wang, Yanliang Zhang, Peng Gao, and Shiyong Shuang %T Comparing Fault Detection Efficiencies of Adaptive Random Testing and Greedy Combinatorial Testing for Boolean-Specifications %0 Journal Article %D 2021 %J Int J Performability Eng %R 10.23940/ijpe.21.01.p11.114122 %P 114-122 %V 17 %N 1 %U {https://www.ijpe-online.com/CN/abstract/article_4532.shtml} %8 2021-01-23 %X

Both random testing and combinatorial testing are input-domain testing techniques. Adaptive random testing, which is an improved version of random testing, selects a test case with more differences from all the existing test cases in each step. Greedy combinatorial testing generates test cases using greedy algorithms to cover more uncovered tuple-combinations of parametric values in each step. To compare fault detection efficiencies of the adaptive random testing technique and greedy combinatorial testing technique, we design an experiment on Boolean specifications that were extracted from the TCAS system. By analyzing fault detection ratios, f-measure values, and APFD values of the two testing techniques, experimental results show that: (1) if the number of test cases is relatively small, fault detection efficiencies of the two techniques are very close though adaptive random testing has a little advantage; (2) for an increase in the number of test cases, the fault detection efficiency of greedy combinatorial testing becomes gradually better.