{"ID":2876685,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21454","arxiv_id":"2508.21454","title":"Enhancing Semantic Understanding in Pointer Analysis using Large Language Models","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.","short_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 larg...","url_abs":"https://arxiv.org/abs/2508.21454","url_pdf":"https://arxiv.org/pdf/2508.21454v1","authors":"[\"Baijun Cheng\",\"Kailong Wang\",\"Ling Shi\",\"Haoyu Wang\",\"Yao Guo\",\"Ding Li\",\"Xiangqun Chen\"]","published":"2025-08-29T09:37:42Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}