Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications

TL;DR

This paper presents an agentic methodology that automatically extracts verification-ready formal properties from unstructured natural language specifications, improving semantic alignment for safety-critical system verification.

Contribution

It introduces a modular pipeline combining requirement extraction, compatibility filtering, and translation, achieving 77.8% accuracy in generating aligned formal properties from informal specs.

Findings

Pipeline achieves 77.8% accuracy in generating formal properties.

Method ensures syntactic and semantic alignment with informal requirements.

Approach bridges the gap between informal specifications and formal verification.

Abstract

Early-stage specifications of safety-critical systems are typically expressed in natural language, making it difficult to derive formal properties suitable for verification and needed to guarantee safety. While recent Large Language Model (LLM)-based approaches can generate formal artifacts from text, they mainly focus on syntactic correctness and do not ensure semantic alignment between informal requirements and formally verifiable properties. We propose an agentic methodology that automatically extracts verification-ready properties from unstructured specifications. The modular pipeline combines requirement extraction, compatibility filtering with respect to a target formalism, and translation into formal properties. Experimental results across three scenarios show that the pipeline generates syntactically and semantically aligned formal properties with a 77.8% accuracy. By explicitly accounting for modeling and verification constraints, the approach is a paving step towards exploiting Artificial Intelligence (AI) to bridge the gap between informal descriptions and semantically meaningful formal verification.