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Towards Continuous Modelling to Enable DevOps: A Preliminary Study with Practitioners
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0416-1787
2022 (English)In: Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings, Association for Computing Machinery, Inc , 2022, p. 774-783Conference paper, Published paper (Refereed)
Abstract [en]

Model-based methods and techniques continuously evolve to meet the increasing challenges of modern-day technical landscapes. Parallel to Model-based methods, other paradigms are similarly maturing and being integrated, and one such paradigm is DevOps. Model-based methods and DevOps are perceived to provide benefits when viewed in isolation. Recently, there has been an increased interest in matching the two paradigms, with various proposals and early adoption results. However, little focus is put on the practitioners' view. In this paper, we propose a methodology that aims to utilise Model-driven engineering and DevOps practices in conjunction. Together with the methodology, we present an early evaluation of it from a practitioner's perspective. In particular, we study a large and long-running student project aiming to build a solar vehicle, by presenting the current integration and potential future directions. In this paper we limit the observation to the development phase. Early feedback from the case study indicates significant benefits for several identified project pain points, and it's expected that more benefits will emerge when more advanced DevOps aspects are integrated with model-based methods, and the project matures. © 2022 Owner/Author.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2022. p. 774-783
Keywords [en]
DevOps, model-based engineering, practitioners, simulink
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-61232DOI: 10.1145/3550356.3561582Scopus ID: 2-s2.0-85142926746ISBN: 9781450394673 (print)OAI: oai:DiVA.org:mdh-61232DiVA, id: diva2:1719162
Conference
ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Available from: 2022-12-14 Created: 2022-12-14 Last updated: 2022-12-14Bibliographically approved

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Bergelin, JohanCicchetti, Antonio

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