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Preguss: It Analyzes, It Specifies, It Verifies
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Preguss: It Analyzes, It Specifies, It Verifies
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Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to context-length limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper outlines Preguss - a modular, fine-grained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by orchestrating two components: (i) potential runtime error (RTE)-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We envisage that Preguss paves a compelling path towards the automated verification of large-scale programs.
Forward citations
Cited by 3 Pith papers
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Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis
Agentic interpretation uses lattices to track LLM judgments on decomposed program claims during analysis.
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Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models
Syntropy synthesises asynchronous multiparty session-type subtypes with 95.6–99.5% checker-accepted validity via LoRA fine-tuning and two-level constrained decoding.
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Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models
A fine-tuned LLM plus a two-level semantic checker synthesizes asynchronous multiparty session type refinements with 95.6–99.5% checker-accepted validity.
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