Resumen
The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While MBSE shows great promise, the ambiguities and inconsistencies present in Natural Language (NL) requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the type (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents an open-source, annotated aerospace requirements corpus (the first of its kind) developed for the purpose of this effort that includes three types of requirements, namely design, functional, and performance requirements. This paper further describes the use of the aforementioned corpus to fine-tune BERT to obtain the aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. Finally, this paper provides a comparison between aeroBERT-Classifier and other text classification models such as GPT-2, Bidirectional Long Short-Term Memory (Bi-LSTM), and bart-large-mnli. In particular, it shows the superior performance of aeroBERT-Classifier on classifying aerospace requirements over existing models, and this is despite the fact that the model was fine-tuned using a small labeled dataset.