Approximating inclusion-based points-to analysis

Abstract

It has been established that achieving a points-to analysis that is scalable in terms of analysis time typically involves trading off analysis precsision and/or memory. In this paper, we propose a novel technique to approximate the solution of an inclusion-based points-to analysis. The technique is based on intelligently approximating pointer- and location-equivalence across variables in the program. We develop a simple approximation algorithm based on the technique. By exploiting various behavioral properties of the solution, we develop another improved algorithm which implements various optimizations related to the merging order, proximity search, lazy merging and identification frequency. The improved algorithm provides a strong control to the client to trade off analysis time and precision as per its requirements. Using a large suite of programs including SPEC 2000 benchmarks and five large open source programs, we show how our algorithm helps achieve a scalable solution.