Adaptive Random Test Case Generation for Combinatorial Testing

Random testing (RT), a fundamental software testing technique, has been widely used in practice. Adaptive random testing (ART), an enhancement of RT, performs better than original RT in terms of fault detection capability. However, not much work has been done on effectiveness analysis of ART in the combinatorial test spaces. In this paper, we propose a novel family of ART-based algorithms for generating combinatorial test suites, mainly based on fixed-size-candidate-set ART and restricted random testing (that is, ART by exclusion). We use an empirical approach to compare the effectiveness of test sets obtained by our proposed methods and random selection strategy. Experimental data demonstrate that the ART-based tests cover all possible combinations at a given strength more quickly than randomly chosen tests, and often detect more failures earlier and with fewer test cases in simulations.