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Blended modeling in commercial and open-source model-driven software engineering tools: A systematic study

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Blended modeling aims to improve the user experience of modeling activities by prioritizing the seamless interaction with models through multiple notations over the consistency of the models. Inconsistency tolerance, thus, becomes an important aspect in such settings. To understand the potential of current commercial and open-source modeling tools to support blended modeling, we have designed and carried out a systematic study. We identify challenges and opportunities in the tooling aspect of blended modeling. Specifically, we investigate the user-facing and implementation-related characteristics of existing modeling tools that already support multiple types of notations and map their support for other blended aspects, such as inconsistency tolerance, and elevated user experience. For the sake of completeness, we have conducted a multivocal study, encompassing an academic review, and grey literature review. We have reviewed nearly 5000 academic papers and nearly 1500 entries of grey literature. We have identified 133 candidate tools, and eventually selected 26 of them to represent the current spectrum of modeling tools.

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The authors would like to thank Patricia Lago and Matthias Tichy for reviewing the protocol, and their constructive remarks. The authors would like to thank the reviewers for their constructive remarks that helped improve the initial manuscript significantly. Last but not least, the authors would like to thank the tool vendors and experts who helped us validate the data in this paper, including Martin Auer, Dominik Bork, Lola Burgueño, Frank Hoffmann, Trevor Jobling, Timothy Lethbridge, Victor Morgante, Staffan Persson, Irene Polikoff, Alessandro Turco, Tamas Szabo, the team, the ETAS team (

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This research was partially funded by the Rijksdienst voor Ondernemend Nederland (RVO) through the ITEA3 BUMBLE project (18006)

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David, I., Latifaj, M., Pietron, J. et al. Blended modeling in commercial and open-source model-driven software engineering tools: A systematic study. Softw Syst Model 22, 415–447 (2023).

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