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Automatic derivation of use case diagrams from interrelated natural language requirements

Published online by Cambridge University Press:  16 May 2024

Simon Schleifer ,
Adriana Lungu ,
Benjamin Kruse ,
Sebastiaan van Putten ,
Stefan Goetz  and
Sandro Wartzack
Simon Schleifer*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Adriana Lungu
Affiliation:
AUDI AG, Germany
Benjamin Kruse
Affiliation:
AUDI AG, Germany
Sebastiaan van Putten
Affiliation:
AUDI AG, Germany
Stefan Goetz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
*
[email protected]

Abstract

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Transferring natural language requirements to use case diagrams helps to avoid inherent ambiguities. However, this is usually a manual, time-consuming task that can be accelerated by utilizing Artificial Intelligence in terms of Natural Language Processing. Thus, this contribution proposes a conceptual framework for automatically grouping interrelated functional requirements and deriving use case diagrams by combining formerly isolated approaches. Moreover, the latter are evaluated by a qualitative potential analysis to support their future industrial application.

Keywords

requirements managementmodel-based systems engineering (MBSE)artificial intelligence (AI)natural language processing (NLP)use case diagrams
Type
Systems Engineering and Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2725 - 2734
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), 2024.

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