  The application of Large Language Models (LLMs) in software engineering,
particularly in static analysis tasks, represents a paradigm shift in the
field. In this paper, we investigate the role that current LLMs can play in
improving callgraph analysis and type inference for Python programs. Using the
PyCG, HeaderGen, and TypeEvalPy micro-benchmarks, we evaluate 26 LLMs,
including OpenAI's GPT series and open-source models such as LLaMA. Our study
reveals that LLMs show promising results in type inference, demonstrating
higher accuracy than traditional methods, yet they exhibit limitations in
callgraph analysis. This contrast emphasizes the need for specialized
fine-tuning of LLMs to better suit specific static analysis tasks. Our findings
provide a foundation for further research towards integrating LLMs for static
analysis tasks.
