Adaptive Random Testing Based on Two-Point Partitioning

Test data generation is a key issue in the field of software testing. Adaptive random testing (ART) method has been proposed by Chen et al. to improve the fault-revealing ability of random testing. In the paper, we are mainly concerned with the partitioning-based adaptive random testing and present a new ART based on two-point partitioning. In the new algorithm, the current max-area region is partitioned by the midpoint of two points instead of a single point. The first point is randomly generated, and the second point is picked out from the candidate set according to the farthest distance criterion. In order to compare our algorithm with other two well-known algorithms, the experiments for the case of two-dimension are performed. The results show that our ART-TPP algorithm has a positive improvement for the other two, i.e. ART-RP and ART-BP. Moreover, the appropriate size of candidate set is determined as 2 or 3 based on our sensitivity analysis.