Improving automation in model-driven engineering using examples

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Martin {Faunes Carvallo}",
  title =        "Improving automation in model-driven engineering using
  school =       "Universite de Montreal",
  year =         "2013",
  address =      "Canada",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 SEARCH-BASED SOFTWARE ENGINEERING, applied sciences -
                 computer science",
  URL =          "",
  bibsource =    "OAI-PMH server at",
  contributor =  "Houari Sahraoui",
  language =     "en",
  oai =          "",
  URL =          "",
  size =         "104 pages",
  abstract =     "This thesis aims to improve automation in Model Driven
                 Engineering (MDE). MDE is a paradigm that promises to
                 reduce software complexity by the mean of the intensive
                 use of models and automatic model transformation (MT).
                 Roughly speaking, in MDE vision, stakeholders use
                 several models to represent the software, and produce
                 source code by automatically transforming these models.
                 Consequently, automation is a key factor and founding
                 principle of MDE. In addition to MT, other MDE
                 activities require automation, e.g. modelling language
                 definition and software migration.

                 In this context, the main contribution of this thesis
                 is proposing a general approach for improving
                 automation in MDE. Our approach is based on
                 meta-heuristic search guided by examples. We apply our
                 approach to two important MDE problems, (1) model
                 transformation and (2) precise modelling languages. For
                 transformations, we distinguish between transformations
                 in the context of migration and general model

                 In the case of migration, we propose a software
                 clustering method based on a search algorithm guided by
                 cluster examples. Similarly, for general
                 transformations, we learn model transformations by a
                 genetic programming algorithm taking inspiration from
                 examples of past transformations.

                 For the problem of precise meta modelling, we propose a
                 meta-heuristic search method to derive well-formedness
                 rules for metamodels with the objective of
                 discriminating examples of valid and invalid

                 Our empirical evaluation shows that the proposed
                 approaches exhibit good results. These allow us to
                 conclude that improving automation in MDE using
                 meta-heuristic search and examples can contribute to a
                 wider adoption of MDE in industry in the coming
  notes =        "Directeur de recherche : Sahraoui, Houari",

Genetic Programming entries for Martin Faunes Carvallo