Adaptive random testing based on flexible partitioning
Chengying Mao, Xuzheng Zhan, Jinfu Chen, Jifu Chen, Rubing Huang · IET Software · 2020
Adaptive random testing (ART) achieves better failure‐detection effectiveness than random testing due to its even spreading of test cases. ART by random partitioning (RP‐ART) is a lightweight method, but its advantage over random testing is relatively low. Although iterative partition testing (IPT) method has good performance for detecting failures in a block pattern, it loses randomness during the test case generation. To overcome the shortcomings of the above two algorithms, a new algorithm named ART by flexible partitioning (FP‐ART) is proposed. In the FP‐ART, a set of random candidates is used to select an appropriate test case by considering their boundary distance. Accordingly, the corresponding sub‐domain is also partitioned by the new test case. Based on this kind of flexible partitioning, the randomness of test case selection can be guaranteed and the spatial distribution of test cases is even more diverse. According to the results in simulation and empirical experiments, FP‐ART demonstrates better failure‐detection effectiveness than RP‐ART and is more suitable to detect the failures in strip patterns than the IPT method. Meanwhile, its failure‐detection ability is much stronger than that of fixed‐size‐candidate‐set ART in the cases of a relatively high failure rate.