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Model-driven engineering (MDE) refers to a range of approaches where models play an indispensable role in software development. Modeling promotes higher level of abstraction, therefore reducing complexity of software development and promoting communication among the several stakeholders in the development process (e.g. product managers, designers, programmers). MDE initiatives, like OMG’s Model-Driven Architecture (MDA), make claims of increased quality and productivity by separating business and application logic from underlying platform technology, transforming models to other models and automating code generation (ranging from system skeletons to complete, deployable products). However, while quality assurance is a well-known topic in “traditional” Software Engineering, less is known on how to assess quality across the MDE lifecycle (encompassing new activities such as metamodel engineering or transformations specification), as well as on the effective improvements obtained by applying MDE itself, face to not using it at all.
We seek novel contributions ranging from conceptual frameworks to case studies on how to leverage ICT systems quality with MDE techniques, as well as how to induce quality assurance in the MDE lifecycle itself.
The suggested topics of interest include, but are not restricted to:
- Quality models in the MDE context
- Quality assurance in the MDE development flow
- Evaluating the quality of models and metamodels
- Models’ traceability throughout the lifecycle
- Assessing quality in model transformations
- Measuring the improvement achieved with an MDE approach, specially regarding quality
- Quality in the context of model-driven service oriented systems
- Case studies and lessons learned in applying MDE in industry
- Empirical studies on the quality of MDE processes
- Modeling and analyzing quality standards
- Role of MDE in the quality evaluation of software maintenance, evolution and migration scenarios
Track Committee
Track Chairs
- Juan Manuel Vara Mesa, Rey Juan Carlos University, Spain
- Marcos Didonet Del Fabro, niversidade Federal do Paraná, Brazil
Program Committee
- Grischa Liebel, School of Computer Science,Reykjavik University, Iceland
- Ivano Malavolta, Vrije Universiteit Amsterdam, Netherlands
- Juan De Lara Jaramillo, Universidad Autónoma de Madrid, España
- Valter V. Camargo, Federal University of São Carlos, Brazil
- Antonio Cicchetti, Mälardalen University, Sweden
- Robert Clarisó, Universitat Oberta de Catalunya, Spain
- Ludovico Iovino, Gran Sasso Science Institute, Italy
- Geylani Kardas, Ege University, Turkey
- Eugene Syriani, University of Montreal, Canada
- Juha-Pekka Tolvanen, Metacase, Finland
- Manuel Wimmer, Technical University Vienna, Austria
- Alberto Silva, Universidade de Lisboa, Portugal
- Vasco Amaral, FCT/UNL, Portugal
Juan M. Vara (@jmvara) is an associate professor at the University Rey Juan Carlos of Madrid (@URJC) where he is a member of the Kybele R&D group and he received his PhD on Computer Science in 2009. Head of the MSc in Information Systems Engineering and the Software and Services Engineering area in the PhD program on ICT.
He has been a doctoral researcher at the University of Nantes - INRIA (@Inria) and a post-doctoral researcher at the European Research Institute in Service Science at the University of Tilburg (@TilburgU_Eng). His current research interests focus on Service Science Management and Engineering, Model-Driven Engineering, software quality and human aspects of Software Engineering.
Marcos Didonet Del Fabro is Associate Professor at Universidade Federal do Paraná, Brazil, at the C3SL Labs. He received his Ph.D. degree in computer science from the University of Nantes, in 2007, in the ATLAS group. He was a Researcher with IBM Software Group, France, on the integration of business rules and model-driven engineering. He did Post-Doctoral studies at ILOG. His current research is about information extraction of imprecise data, model-driven engineering and Open Data Integration. He works on new solutions for integrating large amounts of structured or semi-structured data.
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