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Next Steps in LLM-Supported Java Verification

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arxiv 2502.01573 v1 pith:VKATFW6S submitted 2025-02-03 cs.SE cs.AIcs.LGcs.LO

classification cs.SEcs.AIcs.LGcs.LO
keywords coderigoroustoolsetverificationadvantageallowannotation-basedannotations
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Do AI models help produce verified bug fixes?

    cs.SE 2025-07 conditional novelty 6.0 of 10

    Programmers with LLM access solved fewer formally verified debugging tasks than a no-AI control group, though complete novices and strong language experts gained some benefit.

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