Welcome to MDEBE 2013

Welcome to the First International Workshop on Model-driven Engineering By Example, a satellite event of the MODELS 2013 conference! MDEBE 2013 was a great success! You can find the post-proceedings of all contributions to this workshop at CEUR-WS Vol-1104.

Why Model-driven Engineering By Example?

Examples play a key role in the human learning process. There exist numerous theories on learning styles in which examples are used. Thus the idea of using examples to derive programs has a long tradition in computer science. Like many other domains of software engineering, the model-driven engineering (MDE) community is currently concerned with the use of examples, such as traceability information and different kind of models, to search for solutions that fall within a specified acceptance margin to solve specific problems. Many works are proposed based on learning from examples such as for model transformation, model evolution, model analysis, and model testing. Applying example-based techniques to complex MDE problems necessitates expertise in both, search-based optimization/machine learning algorithms and MDE formalisms and techniques.

Objectives

This is a community-building workshop where the goal is to bring together different researchers working on the application of example-based techniques to solve MDE problems. During the workshop, questions such as the following will be discussed: What does by-example really mean? What do all by-example approaches applied to MDE have in common? What is the current state-of-the-art and the future of MDEBE? Are specific algorithms necessary for MDEBE? What are the lessons learned from applying machine learning and search-based techniques to solve MDE problems? What can be learned from existing by-example approaches in related fields such as data engineering and programming languages?

Topics

We will invite submissions from both academia and industry about any of the following topics of interest, but not limited to (see also the call for papers):

  • Machine learning applied to Model-Driven Engineering
  • Search-based techniques applied to Model-Driven Engineering
  • The use of traceability information to solve Model-Driven Engineering problems
  • Benchmarking of examples-based techniques applied in Model-Driven Engineering
  • Prediction models for Model-Driven Engineering problems
  • New MDE problems that have not been tackled by MDEBE approaches
  • Learning from model repositories
  • Solving case studies by applying by-example approaches
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