Name
Abstract | ||
|---|---|---|
ABSTRACTSymbolic 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3324884.3418902 | ASE |
Keywords | DocType | ISSN |
symbolic execution, search strategy, machine learning | Conference | 1527-1366 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianqi Zhang | 1 | 68 | 21.52 |
| Yufeng Zhang | 2 | 1 | 1.38 |
| Zhenbang Chen | 3 | 199 | 23.60 |
| Ziqi Shuai | 4 | 1 | 1.72 |
| Ji Wang | 5 | 190 | 36.75 |