JDart-Hea: Hybrid Evolutionary Algorithm in Dynamic Symbolic Execution

Symbolic execution is a widely-used program analysis technique. It collects and solves path conditions to guide the program traversing. However, due to the limitation of the constraint solvers, it is difficult to apply symbolic execution on programs with complex path conditions, like nonlinear constraints. In this paper, we propose a dynamic symbolic execution method based on the assistance of hybrid evolutionary algorithm, JDartHea, to solve this problem. We firstly generate an initial population according to the collected path constraints, and then perform crossover and mutation operations to generate new offspring. Secondly, we improve the quality of offspring using simulated annealing algorithm. Population iterations are continuously performed to produce solution that satisfy the path constraints. The experimental results show that JDart-Hea can achieve better coverage on complex programs.