To succeed with the development, deployment, and operation of the new generation of systems developed in the context of digital transformation, cloud, smart technologies, IoT, and 5G, organizations need the agility to adapt to constantly evolving environments to deliver solutions faster and solutions that can be better adapted to the user needs and environments. For this purpose, the systems need to leverage (maximize) the use of available data to be better manage their different aspects.

In the last decade, DevOps has emerged as the prominent approach to increase productivity and system quality in the software industry. It focuses on optimizing the flow of activities involved in the creation of end user value, from idea to deployed functionality and operating systems. It leverages different software development paradigms and techniques such as continuous integration and deployment/delivery, runtime monitoring, analytics, automated testing, and self-adaptiveness. However, in spite of its popularity, many elements of existing DevOps processes remain manual and DevOps still lacks proper engineering frameworks to support continuous improvement. Classicaly, many aspects of DevOps are still implemented in a handmade way by DevOps engineers, and there is no formal support to validate the defined environment, even if they are critical in the product life-cycle.

Model-driven engineering (MDE) aims at increasing productivity and systems quality through the use of models that are understandable and actionable. It enables the use of analysis and generation techniques to automate different parts of the development process. The MDE approach relies on pillars such as dedicated languages definition, formal model reasoning or code generation.

The goal of this workshop is to bring together researchers and practitioners, from both industry and academia, to discuss how MDE and DevOps can complement and contribute to each other.

This workshops serie has the ambition to cover the following topics/questions:

  • Modeling for social good (MODELS 2022 theme):
    • How can DevOps and modelling approaches be combined to deliver more value faster and with better quality to users?
    • How can DevOps and modelling approaches be combined to better integrate data to increase user value?
    • How can DevOps and modelling approaches be combined to better protect personal data?
    • How can DevOps and modelling approaches be combined to better integrate data to improve systems and processes (e.g., using digital twins)?
    • How can models contribute to the integration of new data sources in an open-data context?
    • How can modeling contribute to DevSecOps to ensure security and safety of socio-technical systems?
    • How can modeling and DevOps contribute to increase availability, reliability, safety, security, and usability of systems in general?
  • Modelling for DevOps:
    • Modelling can easily be linked to “dev”, what is its relationship to “ops”?
    • What aspects of DevOps can modeling contribute to?
    • What is the role of modelling in DevOps?
    • What concrete solution (e.g., method, process, tool) can modelling brings to DevOps?
    • What kind of analysis or reasoning techniques are needed by the DevOps approach?
    • How can modelling helps to improve DevOps best practices?
  • DevOps for Modelling:
    • How to monitor a model-driven software in an “ops” context?
    • How can DevOps help in achieving a kind of continuous model-driven engineering process?
    • Are classical MDE tools and techniques ready for DevOps? Can we combine these tools with existing ones?
    • How can DevOps be used to ease the adoption of MDE tools and techniques?
    • What are the requirements for a model-driven tool to be DevOps compatible?
    • What would be the benefits of considering model-driven development in a DevOps way?