![]() | MULTI 2023: 10th International Workshop on Multi-Level Modeling |
Website | https://jku-win-dke.github.io/MULTI2023/ |
Submission link | https://easychair.org/conferences/?conf=multi2023 |
Submission deadline | July 17, 2023 |
The MULTI workshop series is the premier venue for researchers and practitioners working on multi-level modeling and multi-level software development. Multi-level modeling represents a new object-oriented paradigm for both conceptual modelling and software engineering. In contrast to conventional two-level approaches, it supports an unbounded number of classification levels and introduces concepts and mechanims that foster reuse, adaptability, and control. While multi-level languages and tools have reached considerable maturity, the field still offers numerous challenges.
The MULTI workshop series aims at providing a platform for exchanging ideas and promoting further development of multi-level languages, methods, and tools. A particular goal is to encourage the community to, beyond proposing new approaches, analyse different approaches to multi-level modelling and define objective ways to evaluate their respective strengths and weaknesses. Non-exclusive workshop themes in 2023 will be multi-level modeling in education and understanding industry needs.
MULTI 2023 will be held as a satellite event of the MODELS 2023 conference at Västerås Kongress in Västerås, Sweden.
Paper Categories
MULTI 2023 solicits four kinds of papers:
- regular papers (10 pages).
- challenge submissions (10 pages).
- demo papers (5 pages).
- position papers (5 pages).
List of Topics
Topics for regular and position papers include, but are not limited to:
- the nature of elements in a multi-level hierarchy and how to best represent and present them.
- the importance and role of deep characterization mechanisms, including potency and its variants.
- the structure of MLM frameworks.
- fundamental aspects of MLM, such as model composition and decomposition.
- formal approaches to MLM.
- tool support for MLM.
- MLM in education.
- model management (transformation, code generation etc.) in a multi-level setting.
- integration of modelling and programming languages in a multi-level setting.
- constraints in a multi-level setting.
- definition of behavioral semantics in a multi-level setting.
- methods and techniques for discovering clabjects and their specializations and classification relationships.
- design patterns addressing when and how to apply multi-level metamodelling.
- case studies demonstrating advantages of multi-level techniques.
- applying MLM to large and/or real-world problems.
- criteria and approaches for comparing MLM approaches.
Proceedings
All accepted papers will be published by IEEE in a companion volume alongside the main proceedings of MODELS 2023.