Use case identification of natural language system requirements with graph-based clustering
Due to the ever-increasing complexity of technical products, the quantity of system requirements, which are typically expressed in natural language, is inevitably rising. Model-based formalization through the application of Model-based Systems Engineering is a common solution to cope with rising com...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Design Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S205347012510019X/type/journal_article |
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| author | Simon Schleifer Adriana Lungu Benjamin Kruse Sebastiaan van Putten Stefan Goetz Sandro Wartzack |
| author_facet | Simon Schleifer Adriana Lungu Benjamin Kruse Sebastiaan van Putten Stefan Goetz Sandro Wartzack |
| author_sort | Simon Schleifer |
| collection | DOAJ |
| description | Due to the ever-increasing complexity of technical products, the quantity of system requirements, which are typically expressed in natural language, is inevitably rising. Model-based formalization through the application of Model-based Systems Engineering is a common solution to cope with rising complexity. Thereby, grouping requirements to use cases forms the first step towards model-based requirements and allows to improve the understanding of the system. To support this manual and subjective task, automation by artificial intelligence and methods of natural language processing are needed. This contribution proposes a novel pipeline to derive use cases from natural language requirements by considering incomplete manual mappings and the possibility that one requirement contributes to multiple use cases. The approach utilizes semi-supervised requirements graph generation with subsequent overlapping graph clustering. Each identified use case is described by keyphrases to increase accessibility for the user. Industrial requirement sets from the automotive industry are used to evaluate the pipeline in two application scenarios. The proposed pipeline overcomes limitations of prior work in the practical application, which is emphasized by critical discussions with experts from the industry. The proposed pipeline automatically generates proposals for use cases defined in the requirement set, forming the basis for use case diagrams. |
| format | Article |
| id | doaj-art-fb5f1f812dc240c4bafe41aa74eb144f |
| institution | Kabale University |
| issn | 2053-4701 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Design Science |
| spelling | doaj-art-fb5f1f812dc240c4bafe41aa74eb144f2025-08-20T03:28:19ZengCambridge University PressDesign Science2053-47012025-01-011110.1017/dsj.2025.10019Use case identification of natural language system requirements with graph-based clusteringSimon Schleifer0https://orcid.org/0009-0004-4178-2739Adriana Lungu1Benjamin Kruse2Sebastiaan van Putten3Stefan Goetz4https://orcid.org/0000-0002-0326-9158Sandro Wartzack5https://orcid.org/0000-0002-0244-5033Engineering Design, https://ror.org/00f7hpc57Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyTechnical Development, https://ror.org/02aykj333 AUDI AG , Ingolstadt, GermanyTechnical Development, https://ror.org/02aykj333 AUDI AG , Ingolstadt, GermanyTechnical Development, https://ror.org/02aykj333 AUDI AG , Ingolstadt, GermanyEngineering Design, https://ror.org/00f7hpc57Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyEngineering Design, https://ror.org/00f7hpc57Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDue to the ever-increasing complexity of technical products, the quantity of system requirements, which are typically expressed in natural language, is inevitably rising. Model-based formalization through the application of Model-based Systems Engineering is a common solution to cope with rising complexity. Thereby, grouping requirements to use cases forms the first step towards model-based requirements and allows to improve the understanding of the system. To support this manual and subjective task, automation by artificial intelligence and methods of natural language processing are needed. This contribution proposes a novel pipeline to derive use cases from natural language requirements by considering incomplete manual mappings and the possibility that one requirement contributes to multiple use cases. The approach utilizes semi-supervised requirements graph generation with subsequent overlapping graph clustering. Each identified use case is described by keyphrases to increase accessibility for the user. Industrial requirement sets from the automotive industry are used to evaluate the pipeline in two application scenarios. The proposed pipeline overcomes limitations of prior work in the practical application, which is emphasized by critical discussions with experts from the industry. The proposed pipeline automatically generates proposals for use cases defined in the requirement set, forming the basis for use case diagrams.https://www.cambridge.org/core/product/identifier/S205347012510019X/type/journal_articleModel-based Systems Engineering (MBSE)Natural Language Processing (NLP)Overlapping Graph ClusteringRequirements EngineeringUse Case Identification |
| spellingShingle | Simon Schleifer Adriana Lungu Benjamin Kruse Sebastiaan van Putten Stefan Goetz Sandro Wartzack Use case identification of natural language system requirements with graph-based clustering Design Science Model-based Systems Engineering (MBSE) Natural Language Processing (NLP) Overlapping Graph Clustering Requirements Engineering Use Case Identification |
| title | Use case identification of natural language system requirements with graph-based clustering |
| title_full | Use case identification of natural language system requirements with graph-based clustering |
| title_fullStr | Use case identification of natural language system requirements with graph-based clustering |
| title_full_unstemmed | Use case identification of natural language system requirements with graph-based clustering |
| title_short | Use case identification of natural language system requirements with graph-based clustering |
| title_sort | use case identification of natural language system requirements with graph based clustering |
| topic | Model-based Systems Engineering (MBSE) Natural Language Processing (NLP) Overlapping Graph Clustering Requirements Engineering Use Case Identification |
| url | https://www.cambridge.org/core/product/identifier/S205347012510019X/type/journal_article |
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