Year: 2026
Type: conference-paper
Abstract: Pointer analysis is the fundamental technique for compiler optimization and program analysis. Flow sensitive pointer analysis provides high precision but hard to scale to large-size projects. Tailored for rapid iteration scenarios where software evolves continuously, we introduce IncSFS, the first incremental full-sparse flow-sensitive pointer analysis algorithm for C/C++ programs. The algorithm contains two main steps. It first transforms the value-flow graph of a program into a constraint graph, on which a strongly-connected component detection is performed to ensure precision. It then propagates increases and decreases in points-to set in an interleaving manner to support both code deletion and insertion within a single analysis pass. IncSFS is guaranteed to terminate and compute the least fixed point when point-to relation during the analysis is object-acyclic. Experimental results on six large-scale real-world projects show that IncSFS is both precise and efficient, achieving average speedups of 9.60 x over full flow-sensitive pointer analysis and of 5.84 x over the traditional reset-recompute incremental approach. Furthermore, IncSFS improves efficiency by 15.8% compared with state-of-the-art incremental pointer analysis algorithms that also propagate changes in points-to sets.
Authors: Kunlin Liu, Zhenbang Chen, Piyi Zu, Yide Du, Ji Wang
Institution: National University of Defense Technology
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G41279851, G4338802390
OpenAlex ID: W7204265340
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