Next Steps in LLM-Supported Java Verification
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 3 Feb 2025.
Builds on
9 of 38 references.
References · 9
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023Clover: Closed-Loop Verifiable Code Generationcited by 62
- 2023Lemur: Integrating Large Language Models in Automated Program Verificationcited by 49
- 2023Finding Inductive Loop Invariants using Large Language Modelscited by 35
- 2023Ranking LLM-Generated Loop Invariants for Program Verificationcited by 33
- 2023Can ChatGPT support software verification?cited by 14
- 2023Leveraging Large Language Models for Automated Proof Synthesis in Rustcited by 11
- 2024Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AIcited by 3
- 2014OpenJML: Software verification for Java 7 using JML, OpenJDK, and Eclipsecited by 1
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.