Translating Natural Language Requirements to Formal Specifications: A Study on GPT and Symbolic NLP

Software verification is essential to ensure dependability and that a system or component fulfils its specified requirements. Natural language is the most common way of specifying requirements, although many verification techniques such as theorem proving depend upon requirements being written in formal specification languages. Automatically translating requirements into a formal specification language is a relevant and challenging research question, because developers often lack the necessary expertise. In our work we consider the application of natural language processing (NLP) to address that research question. This paper considers two distinct approaches to formalise natural language requirements: a symbolic method and a GPT-based method. The two methods are evaluated with respect to their ability to generate accurate Java Modeling Language (JML) from textual requirements, and the results show good promise for automatic formalisation of requirements.