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MoDeVVa 2021 : 18th Workshop on Model-Driven Engineering, Verification, and Validation (MoDeVVA)


When Oct 10, 2021 - Oct 15, 2021
Where Virtual (Fukuoka, Japan)
Submission Deadline Jul 19, 2021
Notification Due Aug 9, 2021
Final Version Due Aug 16, 2021
Categories    software verification   software validation   model-based engineering   model-based software develop.

Call For Papers


18th Workshop on Model-Driven Engineering, Verification, and Validation (MoDeVVA) Co-located with MODELS 2021

10-15 October 2021

Virtual (Fukuoka, Japan)


Workshop Summary
Models are purposeful abstractions of systems and their environments. They can be used to understand, simulate, and validate complex systems at different abstraction levels. Thus, the use of models is of increasing importance for industrial applications. Model-Driven Engineering (MDE) is a development methodology that is based on models, metamodels, and model transformations. The shift from code-centric software development to model-centric software development in MDE opens up promising opportunities for the verification and validation (V&V) of software. On the other hand, the growing complexity of models and model transformations requires efficient V&V techniques in the context of MDE.

The workshop on Model-Driven Engineering, Verification, and Validation (MoDeVVa) offer a forum for researchers and practitioners who are working on V&V and MDE. The main goals of the workshop are to identify, investigate, and discuss the mutual impacts of MDE and V&V.

For the 2021 edition of the MoDeVVa workshop, we would like to encourage papers addressing the use of AI techniques such as machine learning, to help address the challenges of model-based V&V, while continuing to welcome work in all areas in the intersection between MDE and V&V.


Modelling is a powerful technique for handling the complexity of software and hardware artifacts and their respective environments. Model-Driven Engineering (MDE) provides efficient tools for building and working with models, from the requirements specification of a system to code-generation, testing, configuration, and deployment. Through the systematic use of digital models, which can be processed automatically by programs, MDE offers the opportunity to verify and validate every step in the life cycle of a system. Thus, the first motivation for MoDeVVa is the integration of verification and validation (V&V) techniques into MDE.

While V&V can be seen as an enabler in MDE, it presents a set of challenges of its own. These challenges include issues of usability and integration with MDE processes as well as the technical difficulties of performing V&V tasks.

One way of addressing these challenges is by taking advantage of MDE itself in V&V tasks, for example by means of domain-specific modeling languages (DSMLs) to capture requirements, system properties, specifications, and system design, and leveraging all MDE has to offer such as abstraction, refinement, model-transformations and other techniques, to help perform V&V tasks. Thus, the second motivation for MoDeVVa is the integration of MDE techniques into V&V.

Another way of addressing the challenges posed by V&V in MDE is to leverage novel techniques from AI. The advent of practical machine learning techniques and frameworks opens the way for novel approaches to model-based V&V, which are poised to improve the usability and range of V&V. Thus, the third motivation for MoDeVVa is the integration of novel approaches to the challenges presented by V&V and MDE.

Both MDE and V&V intend to help solve “real-world” problems. Real-world problems and systems are complex. Both MDE and V&V propose approaches to tackle such complexity. Thus, the fourth motivation for MoDeVVa is the applicability of MDE and V&V to complex, real-world problems.


The overall objective of the MoDeVVa workshop is to bring together researchers and practitioners in the domain of V&V and MDE so that the key issues in the integration of MDE and V&V can be identified and solved.

More concretely, MoDeVVa’s main objectives are to address the following questions:

1) How can V&V tools and techniques be integrated into MDE in such a way that expertise in V&V is not required in order to obtain the benefits that V&V offers?

2) How can MDE be leveraged to facilitate V&V tasks?

3) How can novel approaches such as Machine-Learning be leveraged to facilitate V&V in MDE?

4) How can the combination of MDE and V&V help to address the development of complex real-world systems?

Topics of Interest
We welcome contributions in all areas at the intersection of MDE and V&V. This year, we would like to encourage papers related to the application of AI techniques such as Machine Learning in V&V and MDE.

V&V in MDE

• Analysis and V&V of models, meta-models, and model transformations.
• V&V in different stages of the development process (requirements, design, code generation, testing, configuration, deployment).
• Reducing the gap between V&V techniques and MDE.
• Integrating V&V into MDE.
• The application and combination of different V&V techniques (e.g., classical testing, static analysis, model checking, deductive approaches, runtime verification) to MDE artifacts.

MDE in V&V

• Defining V&V approaches that rely on MDE.
• Use of models, meta-models, model transformations, and modelling languages in V&V.
• Use of model-evolution approaches to enable incremental V&V.

Tools, usability, applications

• Integration between modelling tools and IDEs and formal verification back-ends.
• Tools and techniques that help to make use of V&V easier and more applicable to “real-world” problems.
• Tools and techniques that help reduce the semantic gap between V&V formalisms and MDE languages.
• Applications of V&V to MDE.
• Applications of MDE to V&V.
• “Real-world” case studies and applications.

AI-related topics for V&V activities

• Use of Machine Learning (ML) to assist model-based V&V activities (e.g., testing selection, generation and prioritization)
• Current practices/case studies/experience reports on applying ML-assisted model-based V&V.
• ML-assisted test automation for performance testing.
• AI-enabled frameworks/processes for model-based testing to support V&V activities
• From manual testing to intelligent test automation for V&V activities.
• Tools and techniques using AI for performing V&V activities.


• Theoretical frameworks for the integration of V&V and MDE.
• Formalisms and theories for the specification and verification of models.
• Formal approaches to models, modelling languages, including DSMLs and MDE in general.
• Modelling relations for checking model conformance and/or refinement.

Important Dates
Papers submission via EasyChair:
IMPORTANT: All papers must be submitted in IEEE format. Templates for the format can be obtained here:
Short papers should be 5 pages long and long papers should be 10 pages long, references included.

All papers (short and long) should be submitted by July 19th, 2021.


Workshop Format

MoDeVVa 2021 will include an opening keynote, paper presentations and the last session of the day will be dedicated to discussions on the topics presented with the goal of identifying common themes, interesting problems, and shared interests and hopefully, propose avenues for future research. We anticipate an enjoyable and exciting event where all participants will leave with answers or well-founded doubts on MDE and V&V.


Important Dates

Full Paper Submission (both short and long papers): Monday, July 19th, 2021
Notification to authors: Monday, August 9th, 2021
Final version: Monday, August 16th, 2021
Workshop: October 10th-15th, 2021 (to be confirmed)

Organization, Steering & Program Committee


- Raquel A. Oliveira (University of Toulouse, France)
- Iulian Ober (University of Toulouse, France)
- Saad Bin Abid (Fortiss, Germany)

For further information, please send us an email


Steering Committee
- Benoit Baudry (KTH Royal Institute of Technology, Sweden)
- Michalis Famelis (Université de Montreal, Canada)
- Christophe Gaston (CEA, France)
- Levi Lucio (Airbus defence and space GmbH, Germany)
- Frederic Boulanger (Supelec, France)
- Stephan Weissleder (Thales Transportation Systems GmbH, Germany)
- Martina Seidl (Johannes Kepler University Linz, Austria)

Program Committee
- Adina Aniculaesei (Institute for Software and Systems Engineering, TU Clausthal, Germany)
- Alexandre Albore (ONERA)
- Vincent Aravantinos (Autonomous Intelligent Driving GmbH)
- Saad Bin Abid (fortiss GmbH)
- Mira Balaban (Ben-Gurion University of the Negev)
- Frédéric Boulanger (CentraleSupélec)
- Fabrice Bouquet (University of Franche-Comte)
- Loli Burgueño (Open University of Catalonia & CEA LIST)
- Chih-Hong Cheng (DENSO)
- John Derrick (University of Sheffield)
- Michalis Famelis (University of Montreal)
- Sebastian Herzig (Microsoft)
- Leen Lambers (Hasso-Plattner-Institut, Universität Potsdam)
- Mercedes Merayo (Universidad Complutense de Madrid)
- Marius Minea (University of Massachusetts at Amherst, previously Politehnica University Timisoara)
- Iulian Ober (University of Toulouse, IRIT)
- Raquel Araujo Oliveira (Université Paul Sabatier, IRIT)
- Ernesto Posse (Zeligsoft)
- Dehla Sokenou (WPS - Workplace Solutions)
- Maria Spichkova (RMIT University)
- Manuel Wimmer (Johannes Kepler University Linz)

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