# Natural Language Processing for Requirements Formalization: How to Derive New Approaches?

arXiv: https://arxiv.org/abs/2309.13272 · HTML: https://mlsanity.com/paper/2309.13272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [Latent Predictor Networks for Code Generation](https://mlsanity.com/paper/1603.06744/) (2016)
- [Testing a Battery Management System via Criticality-based Rare Event Simulation](https://mlsanity.com/paper/2107.00530/) (2021)
- [FRETting about Requirements: Formalised Requirements for an Aircraft Engine Controller](https://mlsanity.com/paper/2112.04251/) (2021)

## Abstract (arXiv)

It is a long-standing desire of industry and research to automate the software development and testing process as much as possible. In this process, requirements engineering (RE) plays a fundamental role for all other steps that build on it. Model-based design and testing methods have been developed to handle the growing complexity and variability of software systems. However, major effort is still required to create specification models from a large set of functional requirements provided in natural language. Numerous approaches based on natural language processing (NLP) have been proposed in the literature to generate requirements models using mainly syntactic properties. Recent advances in NLP show that semantic quantities can also be identified and used to provide better assistance in the requirements formalization process. In this work, we present and discuss principal ideas and state-of-the-art methodologies from the field of NLP in order to guide the readers on how to create a set of rules and methods for the semi-automated formalization of requirements according to their specific use case and needs. We discuss two different approaches in detail and highlight the iterative development of rule sets. The requirements models are represented in a human- and machine-readable format in the form of pseudocode. The presented methods are demonstrated on two industrial use cases from the automotive and railway domains. It shows that using current pre-trained NLP models requires less effort to create a set of rules and can be easily adapted to specific use cases and domains. In addition, findings and shortcomings of this research area are highlighted and an outlook on possible future developments is given.