Combining Search-Based Testing and Dynamic Symbolic Execution by Evolvability Metric

Abstract

In the area of software testing, search-based software testing (SBST) and dynamic symbolic execution (DSE) are two efficient testing techniques for test cases generation. However, both of the two approaches have their own drawbacks: The efficiency of SBST depends on the guidance of the fitness landscape. When the fitness landscape has some plateaus with no gradient for directing search process, SBST may degenerate into random testing. DSE relies on the capability of constraint solvers. It may struggle to generate test cases with constraints that are difficult to be solved. In this paper, we combine the strengths of both techniques. SBST is used to help DSE for solving difficult constraints and DSE is used to improve the efficiency and capability of SBST. Evolvability metric is introduced for measuring when the software is not suitable for SBST. A novel switch mechanism based on the evolvability metric between SBST and DSE is proposed in this paper to help to choose the proper technique at the proper time. Experiments on several benchmarks reveal the promising results of our proposal.