Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications
Alberto Tagliaferro, Bruno Guindani, Livia Lestingi, Matteo Rossi
TLDR
This paper introduces an agentic LLM-based pipeline to automatically formalize unstructured safety-critical system requirements for verification, achieving 77.8% accuracy.
Key contributions
- Proposes an agentic LLM pipeline for formalizing unstructured safety-critical system requirements.
- Combines requirement extraction, compatibility filtering, and formal property translation.
- Achieves 77.8% accuracy in generating syntactically and semantically aligned formal properties.
- Explicitly accounts for modeling and verification constraints in the formalization process.
Why it matters
Formalizing safety-critical system requirements is crucial but challenging. This paper offers an AI-driven solution that bridges the gap between informal natural language and verifiable formal properties. Its agentic approach ensures semantic alignment, significantly advancing automated verification.
Original 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.
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