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Grand Challenges in Model-Driven Engineering : an Analysis of the State of the Research This is a repository copy of Grand challenges in model-driven engineering : an analysis of the state of the research. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/162633/ Version: Published Version Article: Bucchiarone, Antonio, Cabot, Jordi, Paige, Richard F. orcid.org/0000-0002-1978-9852 et al. (1 more author) (2020) Grand challenges in model-driven engineering : an analysis of the state of the research. Software and Systems Modeling. pp. 5-13. ISSN 1619-1366 https://doi.org/10.1007/s10270-019-00773-6 Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Software and Systems Modeling (2020) 19:5–13 https://doi.org/10.1007/s10270-019-00773-6 EXPERT VOICE Grand challenges in model-driven engineering: an analysis of the state of the research Antonio Bucchiarone1 · Jordi Cabot2 · Richard F. Paige3,4 · Alfonso Pierantonio5 Received: 21 November 2019 / Accepted: 4 December 2019 / Published online: 6 January 2020 © The Author(s) 2020 Abstract In 2017 and 2018, two events were held—in Marburg, Germany, and San Vigilio di Marebbe, Italy, respectively—focusing on an analysis of the state of research, state of practice, and state of the art in model-driven engineering (MDE). The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen. This article reports on the results of those meetings, and presents a set of grand challenges that emerged from discussions and synthesis. These challenges could lead to research initiatives for the community going forward. Keywords Model-driven engineering · Grand challenge · Research roadmap 1 Introduction ever, this success has led to an even higher demand for better tools, theories, and general awareness about model- The field of model-driven engineering [1] (MDE) has ing, its scope, and application. The changing face of MDE is evolved substantially from the earliest work on UML in reflected in the surveys (both broad and specific), roadmaps, the 1990s, through to seminal research on metamodeling, and research challenge workshops found in the literature [2– model transformation, and model management in the early- 5]. to-mid-2000s. MDE has made incredible contributions to In 2017 and 2018, the research community held two leverage abstraction and automation in almost every area of events: the Grand Challenges in MDE workshop,1 co- software and systems development and analysis. In many located with STAF 2017 in Marburg, Germany in July 2017; domains, including railway systems, automotive, business and the Winter Modeling Meeting,2 held in San Vigilio di process engineering, and embedded systems, models are key Marebbe, Italy, in January 2018. Experts from industry and to success in modern software engineering processes. How- academia attended these meetings and presented their views that reflected on the research roadmaps of the past, and the Communicated by Bernhard Rumpe. challenges facing the community in the future. This article attempts to synthesize the discussion at these two meet- B Richard F. Paige [email protected] ings. Moreover, it outlines how far the community has come in addressing challenges presented in previously published Antonio Bucchiarone [email protected] research roadmaps (particularly [3,4]), and what new chal- lenges have arisen in the intervening years. The meetings Jordi Cabot [email protected] were attended by an overlapping set of experts with different backgrounds and experience. The Grand Challenges in MDE Alfonso Pierantonio [email protected] 2017 workshop was a traditional workshop with paper sub- missions and presentations, along with extensive discussion. 1 Fondazione Bruno Kessler, Trento, Italy The Winter Modeling Meeting 2018 was an invitation-only 2 ICREA and UOC, Barcellona, Spain workshop with focused sessions on research challenges as 3 University of York, York, UK 4 McMaster University, Hamilton, Canada 1 http://www.edusymp.org/Grand2017/en. 5 Università degli Studi di L’Aquila, L’Aquila, Italy 2 http://eventmall.info/AMM2018/. 123 6 A. Bucchiarone et al. well as educational challenges facing MDE. The interested Object Constraint Language (OCL),8 and Query-View- reader can find lists of participants and papers for these work- Transformation (QVT)9 specifications. Researchers in mod- shop on the aforementioned websites. eling engaged in a significant way with relevant standardiza- This article attempts to summarize the discussion of world tion efforts, with varying degrees of success. In essence, this experts in MDE at these two venues, capturing a vision of period laid the groundwork for more recent research, provid- the grand challenges facing the community. As such, it may ing the foundations needed for more advanced research on provide useful context for future research projects, research modeling tools and model management. grant proposals, or presentations made to funding bodies. The paper starts with a brief reflection on research challenges 2.2 Challenges from 2007 through present day identified in previous roadmaps, in order to contextualize the new challenges. Then, the paper summarizes the key chal- More recently, various research roadmaps [4,5,11] identified lenges identified during discussions at the Grand Challenges a variety of significant research challenges, many of which in MDE 2017 workshop and the Winter Modeling Meeting have seen substantial research effort. The key issues that were 2018. It then concludes with a brief summary of where the identified in these previously published roadmaps include the authors believe the field of MDE research is going. following – Language engineering (e.g., [12]) principles, practices, 2 Analysis of past challenges and patterns for specifying abstract and concrete syn- tax, as well as semantics. Research challenges related There has been substantial progress in research on MDE to understanding the language engineering process were since the late 1990s and early 2000s. This was analyzed and also identified. the state of the art synthesized, in a selection of research – Language workbenches (popularized in 2005)—i.e., tools roadmaps and challenge papers published at the time. In for defining and composing domain-specific languages this section, we briefly reflect on past research challenges in and their IDEs: the fundamental research against this MDE, in order to contextualize the results of the two work- challenge led to the development of modern language shops. workbenches such as JetBrains MPS,10 Xtext,11 Ker- meta,12 Racket13 and Spoofax.14 2.1 Pre-2007 challenges – Model management—processes and tasks for manipulat- ing and analyzing models: the fundamental research in The period from the late 1990s through to around 2007 was this area led to theoretical results (e.g., identification of dominated by modeling language issues. This was a time different model management tasks, such as model merg- where the Unified Modeling Language (UML) was under- ing and comparison) as well as technical contributions 15 going considerable changes to its semantics, infrastructure (e.g., model management platforms such as AtlanMod 16 and superstructure, and there was a very substantial body and Epsilon ). of research considering precise semantics of such modeling – Model analysis the challenge of techniques for analyz- languages, as well as the metamodeling process [6–9]. The ing models (e.g., for performance or correctness [13]), key use case for modeling was code generation, as embodied along with principles relate to understanding what makes by research on model-to-text transformation languages [10] a good model. and standards,3 and the popularity of code generators that – Models at runtime the use of models to manage and were offered “out of the box” in modeling tools, such as the understand systems after they have been deployed and as Kennedy Carter (now Abstract Solutions)4 or Artisan (now they execute behavior [14]. Substantial research has taken PTC Integrity Modeler)5 tools. place regarding this challenging to identify techniques This was also a period with substantial effort in stan- dardization, which led to the production of the Meta-Object 8 https://www.omg.org/spec/OCL/About-OCL/. 6 7 Facility (MOF), Model-Driven Architecture (MDA), the 9 https://www.omg.org/spec/QVT/About-QVT/. 10 https://www.jetbrains.com/mps/. 11 3 https://www.omg.org/spec/MOFM2T/1.0/. https://www.eclipse.org/Xtext/. 12 4 https://abstractsolutions.co.uk/our-services/executable-uml/. http://www.kermeta.org/. 13 5 https://www.ptc.com/en/products/plm/plm-products/integrity- https://racket-lang.org. modeler. 14 http://strategoxt.org/Spoofax. 6 https://www.omg.org/mof/. 15 http://www.atlanmod.org/. 7 https://www.omg.org/mda/. 16 http://www.eclipse.org/epsilon. 123 Grand challenges in model-driven engineering: an analysis of the state of the research 7 and tools for automatically reflecting
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