Out of sight, out of mind: a distance-aware forgetting strategy for adaptive random testing

Abstract

Adaptive random testing (ART) achieves better failure-detection effectiveness than random testing by increasing the diversity of test cases. However, the intention of ensuring even spread of test cases inevitably causes an overhead problem. Although two basic forgetting strategies (i.e. random forgetting and consecutive retention) were proposed to reduce the computation cost of ART, they only considered the temporal distribution of test cases. In the paper, we presented a distance-aware forgetting strategy for the fixed size candidate set version of ART (DF-FSCS), in which the spatial distribution of test cases is taken into consideration. For a given candidate, the test cases out of its “sight” are ignored to reduce the distance computation cost. At the same time, the dynamic adjustment for partitioning and the second-round forgetting are adopted to ensure the linear complexity of DF-FSCS algorithm. Both simulation analysis and empirical study are employed to investigate the efficiency and effectiveness of DF-FSCS. The experimental results show that DF-FSCS significantly outperforms the classical ART algorithm FSCS-ART in efficiency, and has comparable failure-detection effectiveness. Com-pared with two basic forgetting methods, DF-FSCS is better in both efficiency and effectiveness. In contrast with a typical linear-time ART algorithm RBCVT-Fast, our algorithm requires less computational overhead and exhibits the similar failure-detection capability. In addition, DF-FSCS has more reliable performance than RBCVT-Fast in detecting failures for the programs with high-dimensional input domain.