James H. Andrews and Tim Menzies and Felix C. H. Li
Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe Nighthawk, a system which uses a genetic algorithm (GA) to ﬁnd parameters for randomized unit testing that optimize test coverage. Designing GAs is somewhat of a black art. We therefore use a feature subset selection (FSS) tool to assess the size and content of the representations within the GA. Using that tool, we can prune back 90% of our GA’s mutators while still achieving most of the coverage found using all the mutators. Our pruned GA achieves almost the same results as the full system, but in only 10% of the time. These results suggest that FSS for mutator pruning could signiﬁcantly optimize meta-heuristic search-based software engineering tools.