- author = "Martin {Faunes Carvallo}",
- title = "Improving automation in model-driven engineering using examples",
- school = "Universite de Montreal",
- year = "2013",
- address = "Canada",
- month = jun,
- keywords = "genetic algorithms, genetic programming, SBSE, MODEL-DRIVEN ENGINEERING, SOFTWARE ENGINEERING BY EXAMPLES, AUTOMATED SOFTWARE ENGINEERING, METAMODELING, SEARCH-BASED SOFTWARE ENGINEERING, applied sciences - computer science",
-
URL = "
http://en.diro.umontreal.ca/our-department/news/une-nouvelle/news/improving-automation-in-model-driven-engineering-u-7567/",
- bibsource = "OAI-PMH server at amican.webapps1.lac-bac.gc.ca",
- contributor = "Houari Sahraoui",
- language = "en",
- oai = "oai:collectionscanada.gc.ca:QMU.1866/10562",
-
URL = "http://hdl.handle.net/1866/10562",
- 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 transformations.
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 models.
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 years.",
- notes = "Directeur de recherche : Sahraoui, Houari",
