The 8th International Workshop on Multi-Level Modelling
MODELS 2021 - Fully virtual
The deadline for paper submission has been extended to the 30th of July.
The paper submission page: https://easychair.org/conferences/?conf=multi2021.
The yearly MULTI workshop is the premier event for researchers and practitioners working on multilevel modelling and multilevel software development.
Together with the Models Conference, MULTI 2021 will be fully virtual and will accommodate remote participation and presentation. All accepted papers presented online will be published by IEEE.
Multilevel language architectures represent a new object-oriented paradigm both for conceptual modelling and software engineering. In contrast to conventional approaches, they allow for an arbitrary number of classification levels and introduce other concepts that foster reuse and adaptability. While multilevel languages and tools have reached a considerable maturity, the field still offers numerous challenges. The MULTI workshop series is dedicated to bring together experts who develop and apply multilevel language technologies as well as those who focus on specific analysis and design methods or on economic aspects of this new paradigm.
Multilevel modelling is an emerging new modelling paradigm that offers exciting new perspectives not only for conceptual modelling, but also for the development of software systems that are integrated with models of themselves. Multilevel DSMLs allow for combining the benefits of economies of scale with the productivity enabled by concepts that were designed for very specific domains. Multilevel modelling has been used successfully in a wide range of projects.
The MULTI series is aimed at providing a platform for exchanging ideas and promoting the further development of multilevel languages, methods and tools. In particular, the goal is to encourage the community to delineate different approaches to multilevel modelling and define objective ways to evaluate their respective strengths and weaknesses.
Three kinds of papers are solicited: regular papers (10 pages), challenge papers (10 pages), and position papers (5 pages).
Topics for regular and position papers include, but are not limited to:
Authors submit their papers as PDF files via Easychair. Submissions must adhere to the IEEE formatting instructions. Challenge papers (see Challenge paper description) must be subtitled “A contribution to the MULTI Collaborative Comparison Challenge”. Accepted papers will be included in the joint workshop proceedings published by the IEEE.
Multi-level modeling addresses the modeling of subject domains that benefit from an explicit recognition of multiple levels of domain representation, such as software development, process modeling, capturing organizational roles, biological taxonomies, product hierarchies, and so on. Over the span of two decades many approaches for multi-level modeling have been proposed, all sharing the goal of extending traditional two-level approaches with constructs and concepts that naturally support multiple levels of domain representation, with the goal to increase model expressiveness while simultaneously reducing model complexity.
Numerous advances in multi-level modeling approaches and tools have, however, lead to a proliferation of available approaches, thus displaying a lack of consensus on what kinds of constructs and concepts provide the best support for multi-level modeling. In part, differences are owed to different application targets or different prioritizations of desirable model properties, yet not all existing differences can necessarily be motivated in this manner. Some differences at both foundational and realization levels may be perfectly justifiable while others may be reconcilable without diminishing effects.
The Collaborative Comparison Challenge aims towards increasing communication between multi-level modeling researchers by encouraging collaborations which may justify and thus clarify the need for existing differences, or, alternatively, lead towards homogenizing multi-level modeling. Previous challenges (the 2017 Bicycle Challenge and the 2019 Process Challenge) already invited researchers to demonstrate their approaches by addressing a set of requirements in a given domain and thus represented essential first steps towards the benchmarking of various approaches.
However, since these challenges only focused on a single approach respectively and did not specifically encourage the contrasting of approaches beyond regular related work discussions, their value in contrasting approaches and fostering a dialogue between researchers was limited. For this reason, the Collaborative Comparison Challenge specifically requires the application of two or more approaches to one domain example and mandates the discussion of commonalities and differences between the approaches in a joint paper authored by proponents of different multi-level modeling approaches. Commonalities and differences should be discussed as they manifest themselves in the treatment of the domain example but also at a more general level. Respective discussion subjects which authors may choose to elaborate on include, but are not limited to, fundamental concepts such as the nature of levels, cross-level relationships, classification vs generalization, deep characterization, the treatment of attributes and operations, and the use of structural and behavioral constraints. Discussions should seek to explore justifications for, and/or potential reconciliations of, fundamental differences rather than surface-level realization choices. An optional avenue towards contributing to the clarification of differences is the formalization of foundational concepts, thereby possibly discovering open questions and/or potential for unification.
All submission requirements and the domain example to use are available from the detailed MULTI 2021 challenge description.
Victorio Albani Carvalho
Federal Institute of Espírito Santo (IFES), Brazil
Budapest University of Technology and Economics, Hungary
Johannes Kepler University Linz, Austria
One full day including: