MODELS 2022 ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems October 23-28, 2022 Montreal, Canada https://conf.researchr.org/home/models-2022
MODELS is the premier conference series for model-based software and systems engineering. Since 1998, MODELS has covered all aspects of modeling, from languages and methods, to tools and applications. Attendees of MODELS come from diverse backgrounds, including researchers, academics, engineers, and industrial professionals. MODELS 2022 is a forum for participants to exchange cutting-edge research results and innovative practical experiences around modeling, modeling languages, and model-based software and systems engineering.
This year’s edition will provide an opportunity for the modeling community to further advance the foundations of modeling, and come up with innovative applications of modeling in emerging areas of cyber-physical systems, embedded systems, socio-technical systems, cloud computing, big data, machine learning, security, open source, and sustainability.
For this year’s edition, the conference has the special theme “Modeling for social good” #MDE4SG. Thus, we especially encourage contributions where model-based engineering intersects with research and applications on, not exclusively, socio-technical systems, tools with social impact, integrating human values, data science, artificial intelligence, digital twins, Industry/Society 5.0, and intelligent systems in general.
**** Important Dates ****
Abstract Submission: May 11, 2022 Paper Submission: May 18, 2022 Author notification: July 12, 2022 Camera Ready Due: August 1, 2022
**** Topics of Interest (but not restricted to) ****
MODELS 2022 seeks submissions on diverse topics related to modeling for software and systems engineering, including, but not limited to: * Foundations of model-based engineering, including definition of syntax and semantics of modeling languages and model transformation languages. * New paradigms, formalisms, applications, approaches, frameworks, or processes for model-based engineering such as low-code/no-code development, digital twins, etc. * Definition, usage, and analysis of model-based generative and re-engineering approaches. * Models@Runtime: model-based monitoring, analysis, and adaptation towards intelligent systems, e.g., with digital shadows or digital twins. * Development of model-based systems engineering approaches and modeling-in-the-large including interdisciplinary engineering and coordination. * Applications of AI to model-based engineering problems including e.g., search-based and machine learning approaches. * Model-based engineering foundations for AI-based systems. * Human and organizational factors in model-based engineering. * Tools, meta-tools, and language workbenches for model-based engineering, including model management and scalable model repositories. * Integration of modeling languages and tools (hybrid multi-modeling approaches). * Evaluation and comparison of modeling languages, techniques, and tools. * Quality assurance (analysis, testing, verification) for functional and non-functional properties of models and model transformations. * Collaborative modeling research to address global and team management issues (e.g., browser-based and cloud-enabled collaboration). * Evolution of modeling languages and related standards. * Evidence-based education research for curricular concerns on modeling topics. * Modeling in software engineering; applications of models to address general software engineering challenges. * Modeling for specific challenges such as collaboration, scalability, security, interoperability, adaptability, flexibility, maintainability, dependability, reuse, energy efficiency, sustainability, and uncertainty. * Modeling with, and for, new and emerging systems and paradigms such as security, cyber-physical systems (CPSs), the Internet of Things (IoTs), cloud computing, DevOps, data analytics, data science, machine learning, big data, systems engineering, socio-technical systems, critical infrastructures and services, robotics, mobile applications, conversational agents, open source software, sustainability and modeling for social good. * Empirical studies of applying model-based engineering for domains such as smart production, smart cities, smart enterprises, smart mobility, smart society, etc.
As in previous years, MODELS 2022 is offering two tracks for technical papers: the Foundations Track and the Practice & Innovation Track. A detailed description on the submission process for both tracks is provided at: https://conf.researchr.org/track/models-2022/models-2022-technical-track
**** FOUNDATIONS TRACK ****
We invite authors to submit high quality contributions describing significant, original, and unpublished results in the following categories:
Technical papers should describe innovative research in modeling or model-driven engineering activities. Papers in this submission category should describe a novel contribution to the field and should carefully support claims of novelty with citations to the relevant literature.
Technical papers are evaluated on the basis of originality, soundness, relevance, importance of contribution, strength of validation, quality of presentation and appropriate comparison to related work. Where a submission builds upon previous work of the author(s), the novelty of the new contribution must be described clearly with respect to the previous work. Technical papers need to discuss clearly how the results were validated (e.g., formal proofs, controlled experiments, rigorous case studies, or simulations). Authors are strongly encouraged to make the artifacts used for the evaluation publicly accessible, e.g., through a Github repository or an alternative that is likely to remain available. There will be an artifact evaluation process, as discussed below.
We solicit short papers that present new ideas and visions. Such papers may describe new, non-conventional model-driven engineering research positions or approaches that depart from standard practice. They can describe well-defined research ideas that are at an early stage of investigation. They could also provide new evidence that common wisdom should be challenged, present new unifying theories about existing modeling research that provides novel insight or that can lead to the development of new technologies or approaches, or apply modeling technology to radically new application areas.
New ideas and vision papers will be assessed primarily on their level of originality and potential for impact on the field in terms of promoting innovative thinking. Hence, inadequacies in the state-of-the-art and the pertinence, correctness, and impact of the idea/vision must be described clearly, even though the new idea need not be fully worked out, and a fully detailed roadmap need not be presented. Authors are strongly encouraged to make the artifacts used for the evaluation publicly accessible, e.g., through a Github repository or an alternative that is likely to remain available. There will be an artifact evaluation process, as discussed below.
**** PRACTICE AND INNOVATION TRACK ****
The goal of the Practice and Innovation (P&I) Track is to fill the gap between foundational research in model-based engineering (MBE) and industrial needs. We invite authors from academia and/or industry to submit original contributions reporting on the development of innovative MBE solutions in industries, public sector, or open-source settings, as well as innovative application of MBE in such contexts. Examples include: * Scalable and cost-effective methodologies and tools * Industrial case studies with valuable lessons learned * Experience reports providing novel insights
Each paper should provide clear take-away value by describing the context of a problem of practical importance, and the application of MBE that leads to a solution.
Evaluation Criteria: A paper in the P&I Track will be evaluated mainly from its practical take-away and the potential impact of the findings. More specifically, * The paper should discuss why the solution to the problem is innovative (e.g., in terms of advancing the state-of-practice), effective, and/or efficient, and what likely practical impact it has or will have; * The paper should provide a concise explanation of approaches, techniques, methodologies and tools employed; * The paper should explain best practices that emerged, tools developed, and/or software processes involved. * Studies reporting on negative findings must provide a thorough discussion of the potential causes of failure, and ideally a perspective on how to solve them. * Authors are encouraged to make the artifacts publicly accessible, e.g., through a Github repository or an alternative that is likely to remain available. There will be an optional artifact evaluation process, as discussed below.
**** Artefact Evaluation ****
After the notification, the authors of accepted papers will be invited to submit their accompanying artifacts (e.g., software, datasets, proofs) to the Artifact Evaluation track to be evaluated by the Artifact Evaluation Committee. Participation in the Artifact Evaluation process is optional and does not affect the final decision regarding the acceptance of papers. Papers that successfully go through the Artifact Evaluation process will be rewarded with a seal of approval included in the papers.
**** Special Issue in SoSyM ****
Authors of best papers from the conference will be invited to revise and submit extended versions of their papers for publication in the Journal of Software and Systems Modeling.