21st Workshop on Model Driven Engineering, Verification and Validation (MoDeVVa 2024)

MoDeVVa will take place in September 2024 in Linz, Austria (co-located with MODELS 2024).

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) offers 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 mutual impacts of MDE and V&V.

For the 2024 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, Process Engineering and Quality Assurance, while continuing to welcome work in all areas in the intersection between MDE and V&V.

Scope

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 includes 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 ad-vantage of MDE itself in V&V tasks, for example by means of domain-specific modelling languages (DSMLs) to capture requirements, system properties, specifications and system de-sign, 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.

Objectives

The overarching objective of the MoDeVVa workshop is to bring together researchers and practitioners in the domain of V&V and MBSE/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:

• 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?

• How can MDE be leveraged to facilitate V&V tasks? 

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

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

• How can MDE be leveraged to facilitate industry to acquire certification for their systems or qualification of their development processes and tools?

• How to deploy V&V in ``lightweight'' modeling environments that do not use explicit metamodeling or heavy modeling infrastructures? 

• How MDE and V&V help in increasing confidence in modern systems involving more and more AI components?

Topics of Interest

We welcome contributions in all areas at the intersection of MBSE/MDE and V&V. Papers addressing the following topics are particularly welcome:
V&V in MBSE/MDE

• Theoretical frameworks and approaches for integration of V&V in MBSE/MDE.

• Formalisms and theories for the specification and verification of models.

• Formal approaches to models, modeling languages, including DSMLs and MDE in general.

• Modeling relations for checking model conformance and/or refinement.

• The application and combination of different V&V techniques (e.g., classical testing, static analysis, model checking, deductive approaches, runtime verification) to MBSE/MDE artifacts.

• V&V in “lightweight” modeling environments that do not use explicit metamodeling or heavy modeling infrastructures

MDE in V&V, Certification and Quality Assurance

• Use of MDE abstractions (models, meta-models, model transformations) in V&V tasks.

• Use of model-evolution approaches to enable incremental V&V.

• Industrial case studies for application of MDE for quality assurance.

• Model-based process engineering to acquire certification.

• Process engineering to support V&V activities.

Tools, usability, and applications

• Integration between modeling tools, IDEs and V&V back-ends.

• Innovative approaches for model-based V&V of “real-world” systems.

• Tools and techniques that help reduce the semantic gap between models and back-end formalisms used in V&V tasks.

• Case studies and applications of V&V in MBSE/MDE.

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)

• AI-enabled model inspection

• AI-enabled frameworks/processes for model-based testing

• Current practices/case-studies/experience reports on applying ML-assisted model-based V&V.

• Use of MDE and V&V in systems that involve AI components

Organizers

• Saad Bin Abid (CARIAD SE/Alten, Germany)

• Jens Kosiol (Philipps-Universit ̈at Marburg, Germany and Universit ̈at Kassel, Germany)

• Rakshit Mittal (University of Antwerp - Flanders Make, Belgium)

• Iulian Ober (ISAE-SUPAERO, Université de Toulouse, France)

• Ernesto Posse (Lumenix/Zeligsoft, Canada)

Contact: modevva@gmail.com