Preguss: It Analyzes, It Specifies, It Verifies

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.

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Published
2025-10-09
DOI
https://doi.org/10.1145/3759425.3763394
Primary Topic
Logic, programming, and type systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Preguss: It Analyzes, It Specifies, It Verifies

Xiao Yi, Shengchao Qin, Mingshuai Chen, Jianwei Yin et al.
Logic, programming, and type systems
article

Preguss: It Analyzes, It Specifies, It Verifies

Xiao Yi, Shengchao Qin, Mingshuai Chen, Jianwei Yin, Z Y Wang, Haokun Li, T.-H. Jonah Lin, Mingqi Yang
article en

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.

Xidian University (CN), Chinese University of Hong Kong (HK), Peking University (CN), Zhejiang University (CN)
Fundamental Research Funds for the Central Universities, Natural Science Foundation of Zhejiang Province
Openalex Percentile: Top 85%
Logic, programming, and type systems
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Preguss: It Analyzes, It Specifies, It Verifies — Xiao Yi, Shengchao Qin, et al. · (2025) | TGRS Research Map | TGRS