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Model-Based System Engineering Adoption in the Vehicular Systems Domain
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-2416-4205
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8461-0230
2022 (English)In: 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 2022Conference paper, Published paper (Refereed)
Abstract [en]

As systems continue to increase in complexity, some companies have turned to Model-Based Systems Engineering (MBSE) to address different challenges such as requirement complexity, consistency, traceability, and quality assurance during system development. Consequently, to foster the adoption of MBSE, practitioners need to understand what factors are impeding or promoting success in applying such a method in their existing processes and infrastructure. While many of the existing studies on the adoption of MBSE in specific contexts focus on its applicability, it is unclear what attributes foster a successful adoption of MBSE and what targets the companies are setting. Consequently, practitioners need to understand what adoption strategies are applicable. To shed more light on this topic, we conducted semi-structured interviews with 12 professionals working in the vehicular domain with roles in several MBSE adoption projects. The aim is to investigate their experiences, reasons, targets, and promoting and impeding factors. The obtained data was synthesized using thematic analysis. This study suggests that the reasons for MBSE adoption relate to two main themes: better management of complex engineering tasks and communication between different actors. Furthermore, engagement, activeness and access to expert knowledge are indicated as factors promoting MBSE adoption success, while the lack of MBSE knowledge is an impeding factor for successful adoption.

Place, publisher, year, edition, pages
2022.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-61140DOI: 10.15439/2022f47ISI: 000904404400122Scopus ID: 2-s2.0-85141215415OAI: oai:DiVA.org:mdh-61140DiVA, id: diva2:1716980
Conference
2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 10 October 2022, Sofia, Bulgaria
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-03-01Bibliographically approved

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Gustavsson, HenrikEnoiu, Eduard PaulCarlson, Jan

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  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
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  • asciidoc
  • rtf