Call for Papers

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 different problems. Much work has been 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. Suggested topics of interest areas include but are not limited to:

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

We invite submissions of technical full papers of up to 10 pages long in Springer LNCS, Short/Position papers of up to 6 pages to briefly present research results or ongoing work, and demo papers of up to 3 pages to present new by-example tools.


MDEBE is seeking original papers, which have not been submitted elsewhere. The proceedings will be published in CEUR and indexed by DBLP.


Important Dates

  • Paper Submission Deadline: 15 July 2013 22 July 2013
  • Author Notification: 23 August 2013
  • Workshop Date: 29 September 2013