A nearest-neighbor divide-and-conquer approach for adaptive random testing

Abstract

• We propose a new ART approach to enhance testing efficacy and efficiency of FSCS- ART. • We report on simulations and empirical studies to investigate the proposed method. • Compared with FSCS-ART, our proposed method is more cost-effective. • Compared with other ART algorithms, our method has similar or even better results. • Our proposed method can alleviate the boundary effect problem of FSCS-ART. Adaptive Random Testing (ART) aims at enhancing the failure detection capability of Random Testing (RT) by evenly distributing test cases over the input domain. Many ART algorithms have been proposed to achieve an even spread of test cases, according to different notations. A well-known ART algorithm, namely Fixed-Size-Candidate-Set ART (FSCS-ART), chooses an element as the next test case such that it is farthest away from previously executed test cases. Previous studies have demonstrated that FSCS-ART could achieve good improvements in test effectiveness over RT, but suffers from some drawbacks such as high computational overhead and boundary effect problem. In this paper, we propose an alternative approach to enhance FSCS-ART, namely Nearest-Neighbor Divide-and-Conquer based ART (NNDC-ART), which combines the concepts of nearest-neighbor and divide-and-conquer . It attempts to reduce the computational overhead of test case generation, and also to alleviate the boundary effect problem; while maintaining the advantages of FSCS-ART. The simulation results show that compared with FSCS-ART, NNDC-ART requires much less computational overhead, and also achieves much better test case distribution; while maintaining better or comparable failure-detection effectiveness. Our empirical studies further show that the proposed approach is much more cost-effective than FSCS-ART. In addition, we further compare NNDC-ART against two enhancements of FSCS-ART based on the partitioning strategy, i.e., ART with Divide-and-Conquer (ART-DC) and ART by Bisection and Localization (ART-B-Loc), and find that our proposed approach can achieve similar or even better testing performances in some scenarios.