Abstract interpretation provides a generic framework to design static analysis, and has been widely applied in analysis of real-world programs. The scalability and precision of these analyses depend largely on the chosen abstract domain. The template polyhedra domain is well-known for offering a compelling trade-off between scalability and precision. However, designing effective templates currently requires intensive manual effort and domain expertise. In this paper, we propose a bottom-up approach based on Large Language Models (LLMs) to automatically generate linear templates for the template polyhedral analysis. By leveraging the advanced code comprehension capabilities of LLMs, our approach automatically generates highly relevant templates. Based on this approach, we have implemented IKOSplus, a program analyzer supporting fully automated context-sensitive and interprocedural template polyhedral analysis, on top of IKOS. We evaluated IKOSplus on a variety of small-scale benchmark programs and complex real-world programs. Experimental results demonstrate that IKOSplus effectively balances analytical precision and scalability in the template polyhedral analysis.

@inproceedings{paperbot12556,
  title = {Fully Automating Template Polyhedral Analysis by Leveraging LLMs},
  author = {Renjie Huang and Liqian Chen and Hongfei Fu and Banghu Yin and Dengping Wei and Ji Wang},
  booktitle = {ASE},
  year = {2026},
  doi = {10.1145/3832783.3837472}
}