The 16th International Workshop on Models@run.time

Call for Papers

The complexity of adapting software during runtime has spawned interest in how models can be used to validate, monitor and adapt runtime behaviour. The use of models during runtime extends the use of modelling techniques beyond the design and implementation phases. The goal of this workshop is to look at issues related to developing appropriate model-driven approaches to managing and monitoring the execution of systems. We aim to continue the discussion of research ideas and proposals from researchers who work in relevant areas such as MDE, software architectures, reflection, and autonomic and self-adaptive systems, and provide a "state-of-the-art" research assessment expressed in terms of challenges and achievements.

The objectives of this year's edition of the models@run.time workshop are:
a) to foster work on novel topics covering fundamental as well as applied research on models@run.time or, in general, work that attempts to apply model-driven techniques at runtime,
b) to bring together researchers from the model-driven software development community and ACSOS community and
c) to discuss the applicability of research results on models@run.time to industrial case studies.

A literature survey on models@run.time has been published in the Software and Systems Modelling Journal in 2019 (here). As a result, this year, we (i) strengthen the focus of the workshop on new hot topics, which are at an early stage of research, and call for new types of submissions as described below.

Topics of Interest

Papers on models@run.time can relate (but are not limited) to the following domains:

• Learning Models/AI: runtime models learned using techniques such as Machine Learning and Bayesian Learning/Inference.
• Self-modelling: approaches able to derive runtime models on-the-fly
• Self-aware, Reflective, and Cognitive Computing making use of runtime models
• Self-Organization, Self-Adaptation, and Organic Computing making use of runtime models
• Big Data: application of models@run.time to (i) reflect and adapt the architecture of components involved in big data processing, and (ii) select source and data (data management) to help achieve the system's goals
• Cyber-physical Systems: hybrid runtime models, e.g., based on Modelica or Ptolemy
• Cloud Computing and DevOps: runtime models for, e.g., multi-tenant systems
• Control theory: approaches applying runtime models in the context of control theory
• Application to other sciences: including, e.g., biology, chemistry, sociology and psychology

We strongly encourage authors to address the following topics in their papers:

• The causal connection between the system and the runtime model, with particular focus on a transaction concept for this causal connection (timing, roll-back ability and data-consistency)
• Distributed models@run.time, i.e., having multiple, interacting systems, each having an own runtime model
• Modular models@run.time, i.e., approaches to improve the modularity of models@run.time systems
• Co-evolving models@run.time, i.e., systematic approaches to synchronize multiple, interacting models@run.time systems
• No papers on executable models, unless they are causally (bi-)connected to a running system .

Submission

The workshop participants will be selected based on their experience and ideas related to this maturing field. You are invited to apply for attendance by sending a full paper (6 pages) on original research, lessons learned from realizing an approach or experiences on transferring a research prototype into practice.

Additionally, you can apply for a lighting talk to present yourself to the community by submitting an abstract only, which will not be published.

All papers must conform to the IEEE formatting guidelines, which can be found at: https://www.ieee.org/conferences/publishing/templates.html. At least three PC members will review each submission. The authors will be notified about acceptance before the ACSOS 2023 early registration deadline.

You can submit your papers via EasyChair here.

Publication

All papers will be published as IEEE proceedings.

Organizers

• Sebastian Götz (main contact) (web), Technische Universität Dresden, Germany
• Nelly Bencomo (web), Durham Universiy, UK

Program Committee

• Luciano Baresi, Politecnico di Milano
• Thais Batista, Federal University of Rio Grande do Norte
• Carlos Cetina, San Jorge University
• Antonio Cicchetti, Mälardalen University
• Federico Ciccozzi, Mälardalen University
• Peter Clarke, Florida International University
• Fabio Costa, Federal University of Goias
• Martina De Sanctis, Gran Sasso Science Institute - GSSI
• Nikolaos Georgantas, INRIA
• Ta'Id Holmes, Google
• Mahdi Manesh, Porsche Digital GmbH
• Lionel Seinturier, University of Lille
• Rui Silva Moreira, Universidade Fernando Pessoa & INESC Porto
• Matthias Tichy, Ulm University
• Norha M. Villegas, Universidad Icesi, Cali, Colombia
• Manuel Wimmer, Johannes Kepler University Linz
• Uwe Zdun, University of Vienna