A Novel Evolutionary Approach For Adaptive Random Testing
Random testing is a low cost testing strategy that can be applied to a wide range of testing problems. While the low cost and straightforward application of random testing are appealing, these benefits must be evaluated against the lower effectiveness due to the generality of the approach. Recently a number of novel techniques, coined Adaptive Random Testing, have sought to increase the effectiveness of random testing by attempting to maximize the testing coverage of the input space. This paper presents the novel application of an evolutionary search algorithm to this problem; it provides results from an extensive simulation study in which the evolutionary approach is compared against the Fixed Sized Candidate Set (FSCS), Restricted Random Testing (RRT), quasirandom testing using the Sobol sequence (Sobol), and random testing (RT) methods. The evolutionary approach was found to be superior to FSCS, RRT, Sobol and RT amongst block patterns-the arena in which FSCS and RRT have been demonstrated most effective. The results amongst fault patterns with increased complexity were demonstrated to be similar to those of FSCS and RRT, and showed a modest improvement over Sobol and RT. A comparison of the asymptotic run-times of the evolutionary search algorithm and the other testing approaches is also considered, providing further evidence that the application of an evolutionary search algorithm is feasible and within the same order of time complexity as the other adaptive random testing approaches.