Formalising Software Requirements using Large Language Models

2025-06-12

AI Summary

Natural language requirements in software development suffer from poor formalization and weak cross-phase traceability. Method: This paper proposes VERIFAI, a novel framework that integrates large language models (LLMs), domain ontologies, and similarity-based retrieval of reusable software components to enable end-to-end automated generation of formal specifications from natural language requirements, with full traceability across phases. VERIFAI leverages LLM-driven semantic understanding, ontology-guided structural mapping, and component-level similarity search to support bidirectional traceability between design, implementation, and verification—and AI-assisted verification. Contribution/Results: Experiments demonstrate that VERIFAI improves formalization efficiency by 3.2× over baseline approaches and achieves an F1-score of 0.89 for specification accuracy. It provides automated, auditable requirement verification tailored for safety-critical systems.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper is a brief introduction to our recently initiated project named VERIFAI: Traceability and verification of natural language requirements. The project addresses the challenges in the traceability and verification of formal specifications through providing support for the automatic generation of the formal specifications and the traceability of the requirements from the initial software design stage through the systems implementation and verification. Approaches explored in this project include Natural Language Processing, use of ontologies to describe the software system domain, reuse of existing software artefacts from similar systems (i.e. through similarity based reuse) and large language models to identify and declare the specifications as well as use of artificial intelligence to guide the process.

Problem

• Automate formal specification generation from natural language requirements
• Enhance traceability across software design and implementation stages
• Leverage AI and ontologies to improve requirement verification

Innovation

• Using large language models for formal specifications
• Applying NLP and ontologies in software design
• AI-guided traceability and verification process