Flow Sensitivity without Control Flow Graph: An Efficient Andersen-Style Flow-Sensitive Pointer Analysis

TL;DR

This paper introduces CG-FSPTA, a novel flow-sensitive pointer analysis method that uses a constraint graph to improve efficiency and scalability while maintaining analysis precision.

Contribution

It proposes a flow-sensitive analysis based on constraint graphs, reducing memory and time costs compared to traditional control flow graph methods.

Findings

Achieves 33.05% memory reduction on average.

Speeds up analysis by 7.27 times.

Maintains comparable precision to existing methods.

Abstract

Flow-sensitive pointer analysis constitutes an essential component of precise program analysis for accurately modeling pointer behaviors by incorporating control flows. Flow-sensitive pointer analysis is extensively used in alias analysis, taint analysis, program understanding, compiler optimization, etc. Existing flow-sensitive pointer analysis approaches, which are conducted based on control flow graphs, have significantly advanced the precision of pointer analysis via sophisticated techniques to leverage control flow information. However, they inevitably suffer from computational inefficiencies when resolving points-to information due to the inherent complex structures of control flow graphs. We present CG-FSPTA, a Flow-Sensitive Constraint Graph (FSConsG) based flow-sensitive pointer analysis to overcome the inefficiency of control-flow-graph-based analysis. CG-FSPTA uses a flow-sensitive variant to leverage the structural advantages of set-constraint graphs (which are commonly used in flow-insensitive pointer analysis) while keeping the flow sensitivity of variable definitions and uses, allowing the incorporation of sophisticated graph optimization and dynamic solving techniques. In this way, CG-FSPTA achieves significant efficiency improvements while keeping the precision of flow-sensitive analysis. Experimental evaluations on benchmark programs demonstrate that CG-FSPTA, significantly reduces both memory usage and execution time while maintaining precision. In particular, by solving in the FSConsG, CG-FSPTA achieves an average memory reduction of 33.05\% and accelerates flow-sensitive pointer analysis by 7.27x compared to the state-of-art method. These experimental results underscore the efficacy of CG-FSPTA as a scalable solution to analyze large-scale software systems, establishing a robust foundation for future advancements in efficient program analysis frameworks.