Generalized Points-to Graphs: A New Abstraction of Memory in the Presence of Pointers

TL;DR

The paper introduces Generalized Points-to Graphs (GPGs), a novel memory abstraction that improves the scalability of flow- and context-sensitive points-to analysis by compactly representing procedure summaries through memory updates and control flow optimizations.

Contribution

It presents GPGs as a scalable, precise abstraction for points-to analysis, incorporating new optimizations like strength reduction, redundancy elimination, and call inlining.

Findings

GPGs are significantly smaller than traditional summaries.

The approach scales to 158kLoC C programs.

GPGs maintain precision while discarding unnecessary control flow.

Abstract

Flow- and context-sensitive points-to analysis is difficult to scale; for top-down approaches, the problem centers on repeated analysis of the same procedure; for bottom-up approaches, the abstractions used to represent procedure summaries have not scaled while preserving precision. We propose a novel abstraction called the Generalized Points-to Graph (GPG) which views points-to relations as memory updates and generalizes them using the counts of indirection levels leaving the unknown pointees implicit. This allows us to construct GPGs as compact representations of bottom-up procedure summaries in terms of memory updates and control flow between them. Their compactness is ensured by the following optimizations: strength reduction reduces the indirection levels, redundancy elimination removes redundant memory updates and minimizes control flow (without over-approximating data dependence between memory updates), and call inlining enhances the opportunities of these optimizations. We devise novel operations and data flow analyses for these optimizations. Our quest for scalability of points-to analysis leads to the following insight: The real killer of scalability in program analysis is not the amount of data but the amount of control flow that it may be subjected to in search of precision. The effectiveness of GPGs lies in the fact that they discard as much control flow as possible without losing precision (i.e., by preserving data dependence without over-approximation). This is the reason why the GPGs are very small even for main procedures that contain the effect of the entire program. This allows our implementation to scale to 158kLoC for C programs.