Leveraging LLMs for Formal Software Requirements -- Challenges and Prospects

TL;DR

This paper discusses the challenges and future prospects of using large language models to automatically generate formal software requirements from informal natural language specifications, aiming to improve software correctness verification.

Contribution

It provides a preliminary synthesis of literature on leveraging LLMs and related techniques to address the challenge of translating informal requirements into formal specifications.

Findings

Identifies key challenges in formal requirements generation from natural language.

Highlights potential of LLMs and NLP techniques in automating formal specification creation.

Outlines future research directions for improving formal methods deployment.

Abstract

Software correctness is ensured mathematically through formal verification, which involves the resources of generating formal requirement specifications and having an implementation that must be verified. Tools such as model-checkers and theorem provers ensure software correctness by verifying the implementation against the specification. Formal methods deployment is regularly enforced in the development of safety-critical systems e.g. aerospace, medical devices and autonomous systems. Generating these specifications from informal and ambiguous natural language requirements remains the key challenge. Our project, VERIFAI^{1}, aims to investigate automated and semi-automated approaches to bridge this gap, using techniques from Natural Language Processing (NLP), ontology-based domain modelling, artefact reuse, and large language models (LLMs). This position paper presents a preliminary synthesis of relevant literature to identify recurring challenges and prospective research directions in the generation of verifiable specifications from informal requirements.