Hybridizing Evolutionary Testing with Artificial Immune Systems and Local Search

Search-based test data generation has been a considerably active research field recently. Several local and global search approaches have been proposed, but the investigation of artificial immune system (AIS) algorithms has been extremely limited. Our earlier results from testing six Java classes, exploiting a genetic algorithm (GA) to measure data- flow coverage, helped us identify a number of problematic test scenarios. We subsequently proposed a novel approach for the utilization of clonal selection. This paper investigates whether the properties of this algorithm (memory, combination of local and global search) can be beneficial in our effort to address these problems, by presenting comparative experimental results from the utilization of a GA (combined with AIS and simple local search (LS)) to test the same classes. Our findings suggest that the hybridized approaches usually outperform the GA, and there are scenarios for which the hybridization with LS is more suited than the more sophisticated AIS algorithm.