Tikayat Ray, A.; Cole, B.F.; Pinon Fischer, O.J.; Bhat, A.P.; White, R.T.; Mavris, D.N. Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models. Systems2023, 11, 352.
Tikayat Ray, A.; Cole, B.F.; Pinon Fischer, O.J.; Bhat, A.P.; White, R.T.; Mavris, D.N. Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models. Systems 2023, 11, 352.
Tikayat Ray, A.; Cole, B.F.; Pinon Fischer, O.J.; Bhat, A.P.; White, R.T.; Mavris, D.N. Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models. Systems2023, 11, 352.
Tikayat Ray, A.; Cole, B.F.; Pinon Fischer, O.J.; Bhat, A.P.; White, R.T.; Mavris, D.N. Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models. Systems 2023, 11, 352.
Abstract
The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and in particular, a shift towards Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies that hinder their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP) in general and Large Language Models (LLMs) in particular can serve as an enabler for the conversion of NL requirements into semi-machine-readable requirements. This is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into semi-machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach is an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs were utilized. Two of these models were fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERT), specifically aeroBERT-NER and aeroBERT-Classifier, which were trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, was utilized to identify sentence chunks present in NL requirements. All three language models were utilized together to achieve the standardization of requirements. To demonstrate the effectiveness of the methodologies, requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs) were employed, and a total of two, five, and three boilerplate templates were identified for design, functional, and performance requirements, respectively.
Keywords
Requirements Engineering; Natural Language Processing; NLP; BERT; Requirements boilerplates; Model-Based Systems Engineering; MBSE; Requirements table; Large Language Models (LLMs); Transformer based language models
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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