Enhancing Semantic Understanding in Pointer Analysis using Large Language Models

• URL: http://arxiv.org/abs/2508.21454v1
• Date: Fri, 29 Aug 2025 09:37:42 GMT
• Title: Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
• Authors: Baijun Cheng, Kailong Wang, Ling Shi, Haoyu Wang, Yao Guo, Ding Li, Xiangqun Chen,
• Abstract summary: We propose LMPA (LLM-enhanced Pointer Analysis), a vision that integrates LLMs into pointer analysis to enhance both precision and scalability.<n>LMPA identifies user-defined functions that resemble system APIs and models them accordingly, thereby mitigating erroneous cross-calling-context propagation.
• Score: 15.896543462798276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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