Static program analysis uses sensitivity to balance between precision and scalability. However, finer sensitivity does not necessar- ily 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 de- signed 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.