Next Steps in LLM-Supported Java Verification
arXiv:2502.01573 · doi:10.1109/NSE66660.2025.00007
Abstract
Recent work has shown that Large Language Models (LLMs) are not only a suitable tool for code generation but also capable of generating annotation-based code specifications. Scaling these methodologies may allow us to deduce provable correctness guarantees for large-scale software systems. In comparison to other LLM tasks, the application field of deductive verification has the notable advantage of providing a rigorous toolset to check LLM-generated solutions. This short paper provides early results on how this rigorous toolset can be used to reliably elicit correct specification annotations from an unreliable LLM oracle.
Accepted to NSE 2025, 1st International Workshop on Neuro-Symbolic Software Engineering (ICSE Workshop), 6 pages, 3 figures