Dynamic partitioning strategy to enhance symbolic execution

Software testing is a fundamental part of the software development process. In the context of embedded-software applications, testing can find defects which cause unprecedented risks. The path explosion problem often necessitates one to consider an extremely large number of paths in order to reach a specific target. Symbolic execution can reduce this cost by using symbolic values and heuristic exploration strategies. Although various exploration strategies have been proposed in the past, the number of SMT solver calls for reaching a target is still large, resulting in long execution times for programs containing many paths. In this paper, we present a dynamic partitioning strategy in order to mitigate this problem, consequently reducing unnecessary SMT solver calls as well. Using this strategy on SSA-applied code, the code sections are analyzed in a nonconsecutive order guided by data dependency metrics within the sections. Experimental results show that our dynamic strategy can achieve significant speedups in reducing the number of unnecessary solver calls in large programs. More than 1000× speedup can be achieved in large programs over conflict-driven learning techniques.