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MODELS 2024 : MODELS 2024 : ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems


Conference Series : Model Driven Engineering Languages and Systems
When Sep 22, 2024 - Sep 27, 2024
Where Linz, Austria
Abstract Registration Due Mar 21, 2024
Submission Deadline Mar 28, 2024
Notification Due Jun 17, 2024
Final Version Due Jul 31, 2024
Categories    models   model-driven engineering   software engineering

Call For Papers



ACM/IEEE 27th International Conference on
Model Driven Engineering Languages and Systems

September 22-27, 2024
Linz, Austria


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 2024 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.


**** Important Dates ****

Abstract Submission: March 21, 2024
Paper Submission: March 28, 2024
Author responses: May 27-29, 2024
Author notification: June 17, 2024
Camera Ready Due: July 31, 2024


**** Topics of Interest (but not restricted to) ****

MODELS 2024 solicits submissions on a variety of topics related to modeling for software and systems engineering including, but not limited to:
* Fundamentals of model-based engineering, including the 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, use, and analysis of model-based generative and re-engineering approaches.
* Model-based monitoring, analysis, and adaptation heading towards intelligent systems.
* Development of model-based systems engineering approaches and modeling-in-the-large, including interdisciplinary engineering and coordination.
* Applications of AI to model-related engineering problems, e.g., approaches based on search, machine learning, large language models (AI for modeling)
* Model-based engineering foundations for AI-based systems (modeling for AI)
* 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.
* Hybrid multi-modeling approaches, i.e., integration of various modeling languages and their tools.
* Evaluation and comparison of modeling languages, techniques, and tools.
* Quality assurance (analysis, testing, verification, fidelity assessment) for functional and non-functional properties of models and model transformations.
* Collaborative modeling to address team management issues, e.g., browser-based and cloud-enabled collaboration.
* Evolution of modeling languages and related standards.
* Modeling education, e.g., delivery methods and curriculum design.
* Modeling in software engineering, e.g., applications of models to address common 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, novel systems and paradigms in fields such as security, cyber-physical systems (CPSs), the Internet of Things, cloud computing, DevOps, blockchain technology, data analytics, data science, machine learning, Big Data, systems engineering, socio-technical systems, critical infrastructures and services, robotics, mobile applications, conversational agents, and open-source software.
* Empirical studies on the application of model-based engineering in areas such as smart manufacturing, smart cities, smart enterprises, smart mobility, smart society, etc.


As in previous years, MODELS 2024 offers two tracks for technical papers:
the Foundations Track and the Practice Track. A detailed description of these tracks can be found at:

NEW THIS YEAR: Foundations Track welcomes both short and long New Ideas and Vision Papers



We invite authors to submit high-quality papers describing significant, original, and unpublished results in the following categories:

1. Technical Papers

Technical papers should report on innovative research in modeling or model-driven engineering activities. They should describe a novel contribution to the field and carefully demonstrate the novelty by referencing relevant related literature.

Evaluation Criteria:

Technical papers will be evaluated based on originality, soundness, relevance, significance, strength of validation, quality of presentation, and quality of related work discussions. Submissions must clearly and explicitly describe what is novel about their contribution in comparison to prior work. Results must be validated by formal proofs, rigorous demonstrations (e.g., rigorous case studies or simulations), or empirical evaluations (e.g., controlled experiments or surveys). Authors are strongly encouraged to make the artifacts used for the evaluation publicly available, e.g., via a GitHub repository or an alternative that is expected to provide long-term availability. A respective artifact evaluation process is described below.

2. New Ideas and Vision Papers

New ideas and vision papers describe original, non-conventional research positions in modeling or model-driven engineering and/or approaches that deviate from standard practice. They describe well-defined revolutionary research ideas that are in the early stage of the investigation. They might provide evidence that common wisdom should be challenged, present unifying theories about existing modeling research that can provide new insights or lead to the development of new technologies or approaches, or apply modeling technology to unprecedented application areas.

Evaluation Criteria:

New ideas and vision papers are either short or long papers. Both will be assessed primarily on their degree of originality and potential for advancing innovation in the field. As such, new ideas and vision papers are expected to follow a specific format, and provide a compelling and revolutionary argument. Note that this category is not intended for foundation or practice papers without sufficient evaluation. Such papers will not be accepted. Submissions must clearly describe shortcomings of the state-of-the-art and the relevance, correctness, and impact of the idea/vision. New ideas and vision papers need not be fully worked out and a detailed roadmap need not be provided. The use of worked-out examples to support new ideas is strongly encouraged. Long papers must also supply some degree of validation. However, we accept less rigorous methods of validation such as compelling arguments, exploratory implementations, and substantial examples.
Authors are also strongly encouraged to make any artifacts publicly available, e.g., via a GitHub repository or an alternative that is expected to provide long-term availability. A respective artifact evaluation process is described below.



The goal of the Practice Track is to bridge the gap between foundational research in Model-Based Engineering (MBE) and needs in practice. We invite authors to submit original contributions that report on the application of MBE solutions in the industry, the public sector, or open-source environments. Examples include:
* Demonstrations of scalable and cost-effective methodologies and tools.
* Case studies or field reports offering valuable insights.
* Comparisons of competing approaches in real-world scenarios.
* Submissions need to communicate the context of the application and the practical importance of the findings. Unlike the application itself, any reported lessons learned or insights gained must be original.

Evaluation Criteria:

A paper in the Practice Track will be evaluated primarily on the potential impact of its findings. Specifically:

* The paper must describe the context of the MBE application and what problem it solves/addresses.
* The paper should include a concise explanation of the approaches, techniques, methodologies, and tools used.
* The paper should report on the efficacy of the application, ideally in comparison to alternatives, and/or what new lessons have been learned or insights have been gained.
* Studies that report negative results must include a thorough discussion of the possible causes of the failure and, ideally, provide a perspective on how to address them.

Authors are encouraged to make artifacts publicly available, e.g., via a GitHub repository or an alternative that is expected to provide long-term availability. A respective artifact evaluation process is described below.


**** Artefact Evaluation ****

Authors of accepted papers will be invited to submit their accompanying artifacts (e.g., software and datasets) 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 paper acceptance. Submissions that successfully pass the Artifact Evaluation process will be awarded a seal of approval that will be attached to 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.

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