一句话：通过轻量级冗余检查与在线学习自适应搜索策略，提升多路符号执行的可扩展性。
符号执行仍面临路径爆炸与约束求解开销导致的可扩展性问题。近期提出的 MuSE 框架通过一次求解生成部分解，从而同时探索多条路径。本文从两方面改进 MuSE：首先引入轻量级检查，减少冗余的部分解，避免产生相同结果的冗余执行；其次采用在线学习，为目标程序设计自适应搜索策略，以更高效地分配路径探索方向。初步实验结果表明，所提出的方法在提升多路符号执行效率方面具有潜力。
Symbolic execution is still facing the scalability problem caused by path explosion and constraint solving overhead. The recently proposed MuSE framework supports exploring multiple paths by generating partial solutions in one time of solving. In this work, we improve MuSE from two aspects. Firstly, we use a light-weight check to reduce redundant partial solutions for avoiding the redundant executions having the same results. Secondly, we introduce online learning to devise an adaptive search strategy for the target programs. The preliminary experimental results indicate the promising of the proposed methods.
DOI 原文 ·
@inproceedings{paperbot5987,
title = {Efficient multiplex symbolic execution with adaptive search strategy},
author = {Tianqi Zhang and Yufeng Zhang and Zhenbang Chen and Ziqi Shuai and Ji Wang},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3418902}
}