Candidate test set reduction for adaptive random testing: An overheads reduction technique

Adaptive Random Testing (ART) is a family of testing techniques that were proposed as an enhancement of random testing (RT). ART achieves better failure-detection capability than RT by more evenly distributing test cases throughout the input domain. However, this process of selecting more diverse test cases incurs a heavy computational cost. In this paper, we propose a new ART method that improves on the efficiency of Fixed-Size-Candidate-Set ART (FSCS) by applying a test set reduction strategy. The proposed method, FSCS by Candidate Test Set Reduction (FSCS-CTSR), reduces the number of randomly generated candidate test cases, but supplements them with earlier, unused candidates that have lower similarity to the executed test cases. Simulations and experimental studies were conducted to examine the effectiveness and efficiency of the method, with the experimental results showing a comparable failure-detection effectiveness to FSCS, but with lower computational costs.