Divide, Conquer and Verify: Improving Symbolic Execution Performance

TL;DR

This paper proposes a divide-and-conquer method for symbolic execution that improves performance by executing program slices separately and then combining results, addressing the path explosion and SMT solving challenges.

Contribution

It introduces a novel divide-and-conquer approach that enhances symbolic execution efficiency by modularizing analysis and reducing complexity.

Findings

Significant reduction in execution time on large programs

Effective mitigation of path explosion problem

Improved scalability of symbolic execution tools

Abstract

Symbolic Execution is a formal method that can be used to verify the behavior of computer programs and detect software vulnerabilities. Compared to other testing methods such as fuzzing, Symbolic Execution has the advantage of providing formal guarantees about the program. However, despite advances in performance in recent years, Symbolic Execution is too slow to be applied to real-world software. This is primarily caused by the \emph{path explosion problem} as well as by the computational complexity of SMT solving. In this paper, we present a divide-and-conquer approach for symbolic execution by executing individual slices and later combining the side effects. This way, the overall problem size is kept small, reducing the impact of computational complexity on large problems.