Improving Failure Detection by Automatically Generating Test Cases Near the Boundaries

Boundary value analysis is a typical conventional testing technique. However, manually identifying input regions and writing test cases are labor-intensive and time-consuming. In this paper, we propose a search-based random testing approach, which automatically generates test data along the boundaries of semantic regions of the input domain. The experiments on mutated programs confirm the effectiveness and efficiency of the proposed approach. Furthermore, our approach significantly outperforms the conventional ART (Adaptive Random Testing) methods, which sample test cases evenly across the input regions. Our approach also outperforms EvoSuite, a state-of-the-art tool that generates test cases satisfying certain coverage criterion.