Improving Random Test Sets Using a Locally Spreading Approach
Xiangyang Huang, LiGuo Huang, Shudong Zhang, Lijuan Marissa Zhou, Minhua Wu, Mingrui Chen · 2017
Labeling test cases is expensive. Given a limited number of test cases to be labeled, more evenly spreading test cases over the input domain has a better chance to hit the nonpoint failure patterns. In this paper, we propose a spreading points (test cases) algorithm based on local layout of points (the locally spreading approach is referred to as LS). The LS repositions the initial test set and evolves it to improve the minimum distance among points. During the locally spreading process, for every point, a feasible direction of movement is solved according to its nearest neighbors, and along this direction the point can increase the shortest pairwise distance between it and other points. We investigate the effective of the LS approach, considering it as an add-on to the Adaptive Random Testing (ART) technique. The simulation results show that the LS can improve effectiveness (P-measure) of the ART.