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AUTOMATED CONDITION DETECTION IN REQUIREMENTS ENGINEERING

Published online by Cambridge University Press:  19 June 2023

Alexander Elenga Gärtner ,
Dietmar Göhlich  and
Tu-Anh Fay
Alexander Elenga Gärtner*
Affiliation:
TU-Berlin; IAV GmbH
Dietmar Göhlich
Affiliation:
TU-Berlin;
Tu-Anh Fay
Affiliation:
TU-Berlin;
*
Gärtner, Alexander Elenga, IAV GmbH, Germany, alexander.elenga.gaertner@iav.de

Abstract

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In product development, it is of great importance that a complete, unambiguous, and, as far as possible, contradiction-free target system is defined. Requirements documents of complex systems can contain several thousand individual requirements, derived in an interdisciplinary manner and written in natural language by many different stakeholders. Hence, errors, in the form of contradictions, cannot be completely avoided in these documents and today they must be corrected manually with high effort.

This paper presents an important building block for automated contradiction detection and quality analysis of requirements documents. We discuss the necessary identification of conditions in requirements and the extraction of the verbal expressions associated with condition and effect, respectively. We applied and analyzed natural language processing methods based on grammatical versus machine learning models. The models have been applied to 1,861 real-world requirements. Both approaches generate promising results, with an accuracy partly over 98%. However, in structured specification texts, a grammatical model is preferable due to lower effort in preprocessing and better usability.

Keywords

RequirementsMachine learningSemantic data processingSystems Engineering (SE)Evaluation
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 707 - 716
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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