Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from their insufficient semantic understanding of code, resulting in overly conservative treatment of user-defined functions. Recent advances in large language models (LLMs) present new opportunities to bridge this gap. In this paper, we propose LMPA (LLM-enhanced Pointer Analysis), a vision that integrates LLMs into pointer analysis to enhance both precision and scalability. LMPA identifies user-defined functions that resemble system APIs and models them accordingly, thereby mitigating erroneous cross-calling-context propagation. Furthermore, it enhances summary-based analysis by inferring initial points-to sets and introducing a novel summary strategy augmented with natural language. Finally, we discuss the key challenges involved in realizing this vision.
@article{arxiv.2508.21454,
title = {Enhancing Semantic Understanding in Pointer Analysis using Large Language Models},
author = {Baijun Cheng and Kailong Wang and Ling Shi and Haoyu Wang and Yao Guo and Ding Li and Xiangqun Chen},
journal= {arXiv preprint arXiv:2508.21454},
year = {2025}
}