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NATURAL LANGUAGE PROCESSING IN REQUIREMENTS ENGINEERING AND ITS CHALLENGES FOR REQUIREMENTS MODELLING IN THE ENGINEERING DESIGN DOMAIN

Published online by Cambridge University Press:  19 June 2023

Judith Sophie van Remmen*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Dennis Horber
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Adriana Lungu
Affiliation:
AUDI AG
Fan Chang
Affiliation:
AUDI AG
Sebastiaan van Putten
Affiliation:
AUDI AG
Stefan Goetz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
*
van Remmen, Judith Sophie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, van_remmen@mfk.fau.de

Abstract

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Requirements represent a central element in product development. The large number of requirements inevitably results in an increased susceptibility to errors, an expenditure of time and development costs. The associated problems motivate the application of Artificial Intelligence in the form of Natural Language Processing (NLP). In Requirements Engineering one main task is the classification of requirements which serves as the input in architectural models e.g. in SysML. In mechanical engineering there is still little overview regarding the interface between requirements classification and modelling. This paper provides an overview of the requirement classes and entities used in the literature and analyses their utilisation in modelling. Existing requirements classes usually do not offer the flexibility to be transferred to other domains. However, basic structures can be adopted from those classifications. This enables a clear assignment of existing classes to object classes in modelling. Resulting from the conducted literature study the observed predominant focus of research on the software industry requires an extension of the existing requirement classes and entities to enable further use and transfer to mechanical engineering.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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