The objective of the MoDeVVa workshop is to bring together researchers and practitioners in the domain of V&V and MDE so that the key V&V issues in MDE can be identified and solved. For instance, with respect to usability, V&V specialists can bring theoretical foundations and approaches that work best in specific cases. MDE specialists can identify common patterns in these V&V approaches and factorize them, making the concepts easier to understand and use in various contexts.

With this year's emphasis, we are looking to encourage researchers to address the usability shortcomings of V&V and MDE which limit their adoption. A further objective is to encourage researchers in V&V and MDE to reach to other fields such as machine learning to stimulate novel approaches to existing problems in these disciplines.

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 2018 edition of the MoDeVVa workshop we would like to propose an emphasis on usability, user friendliness and approaches, tools and techniques that enable the applicability of V&V in MDE as well as the use of MDE in V&V activities. We would additionally like to invite submissions where machine learning is used as an enabler to ease the usage and applicability in practice of modeling and verification techniques.