MoDeVVa 2011 Workshop Summary

Abstract

AbstractAll workshops of the MoDeVVa series are focused on Model-Driven Engineering, Verification, and Validation.Models are purposeful abstractions. They are important elements in established life cycle management approaches, especially in model-driven approaches. Most importantly, they are very useful for communication of important aspects of the software being developed - they are used to support the focus on these important aspects and to make complex systems simple to understand. Furthermore, models are descriptions at an arbitrary level of detail and can, thus, be used to make traceability from requirements to any other object in development easier. If the models are not just sketches, but also have a formal foundation, they can also used for automatic transformation or code generation. Typical applications are the automatic generation of large parts of system source code, which results in a decrease of system engineering costs. One of the most important representatives for the application of models is the Model-Driven Engineering (MDE) approach. MDE is a development methodology that is based on models, meta models, and model transformations. There are many tools to support models, (domain-specific) modeling languages, model transformations, and code generation. The usage of models, model transformations, and code generation is becoming more and more important for industrial applications. The fact that development of MDE-related techniques still moves forward shows that MDE is quickly changing and that new approaches and corresponding issues arise frequently. The focus of the MoDeVVa series is on verification and validation (V&V) techniques in the context of MDE. V&V are currently very relevant in many safety-critical domains, with some of those domains being heavyweight representatives such automotive or avionics.KeywordsModel CheckerModeling LanguageModel TransformationTest OracleWorkshop SeriesThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.