Name
Abstract | ||
|---|---|---|
Static program analysis uses sensitivity to balance between precision and scalability. However, finer sensitivity does not necessarily lead to more precise results but may reduce scalability. Recently, a number of approaches have been proposed to finely tune the sensitivity of different program parts. However, these approaches are usually designed for specific program analyses, and their abstraction adjustments are coarse-grained as they directly drop sensitivity elements. In this paper, we propose a new technique, 4DM, to tune abstractions for program analyses in Datalog. 4DM merges values in a domain, allowing fine-grained sensitivity tuning. 4DM uses a data-driven algorithm for automatically learning a merging strategy for a library from a training set of programs. Unlike existing approaches that rely on the properties of a certain analysis, our learning algorithm works for a wide range of Datalog analyses. We have evaluated our approach on a points-to analysis and a liveness analysis, on the DaCapo benchmark suite. Our evaluation results suggest that our technique achieves a significant speedup and negligible precision loss, reaching a good balance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-88806-0_4 | STATIC ANALYSIS, SAS 2021 |
Keywords | DocType | Volume |
Static analysis, Datalog, Data-driven analysis, Domain-wise merging | Conference | 12913 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
8 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Chen | 1 | 58 | 19.82 |
| Chenyang Yang | 2 | 0 | 0.34 |
| Xin Zhang | 3 | 0 | 0.34 |
| Yingfei Xiong | 4 | 1053 | 55.12 |
| Hao Tang | 5 | 0 | 0.34 |
| Xiaoyin Wang | 6 | 185 | 18.44 |
| Lingming Zhang | 7 | 2726 | 154.39 |