Preguss: It Analyzes, It Specifies, It Verifies

TL;DR

Preguss is a modular framework that combines static analysis and large language models to automate the generation and refinement of formal specifications for large-scale software and hardware verification.

Contribution

It introduces a novel, fine-grained approach that integrates runtime error guidance with LLM-assisted specification synthesis for scalable formal verification.

Findings

Effective integration of static analysis and LLMs for specification generation

Improved scalability in formal verification of large systems

Potential to automate large-scale program verification

Abstract

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.