- author = "Edouard Batot",
- title = "From examples to knowledge in model-driven engineering : a holistic and pragmatic approach",
- school = "Departement d'informatique et de recherche operationnelle, Universite de Montreal",
- year = "2018",
- address = "Canada",
- month = nov,
- keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Model-Driven Engineering, Search Based Software Engineering, Learning from examples, Generalization and Representativeness, Machine Learning, Aritficial Intelligence, Genie logiciel, Apprentissage machine, Intelligence artificielle, Genie logiciel experimental, Apprentissage a partir d'exemples",
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URL = "
https://papyrus.bib.umontreal.ca/xmlui/bitstream/1866/21737/2/Batot_Edouard_2018_These.pdf",
-
URL = "
http://hdl.handle.net/1866/21737",
- size = "169 pages",
-
abstract = "Model-Driven Engineering (MDE) is a software
development approach that proposes to raise the level
of abstraction of languages in order to shift the
design and understanding effort from a programmer point
of view to the one of decision makers. However, the
manipulation of these abstract representations, or
models, has become so complex that traditional
techniques are not enough to automate its inherent
tasks. For its part, the Search-Based Software
Engineering (SBSE) proposes to reformulate the
automation of MDE tasks as optimization problems. Once
reformulated, the problem will be solved by
metaheuristic algorithms. With a plethora of studies on
the subject, the power of automation of SBSE has been
well established.
Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions.
However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions.
We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities.",
-
abstract = "Le Model-Driven Engineering (MDE) est une approche de
developpement logiciel qui propose d'elever le niveau
d'abstraction des langages afin de deplacer l'effort de
conception et de comprehension depuis le point de vue
des programmeurs vers celui des decideurs du logiciel.
Cependant, la manipulation de ces representations
abstraites, ou modeles, est devenue tellement complexe
que les moyens traditionnels ne suffisent plus a
automatiser les differentes taches. De son cote, le
Search-Based Software Engineering (SBSE) propose de
reformuler l'automatisation des taches du MDE comme des
problemes d'optimisation. Une fois reformule, la
resolution du probleme sera effectuee par des
algorithmes metaheuristiques. Face a la plethore
d'etudes sur le sujet, le pouvoir d'automatisation du
SBSE n'est plus a demontrer.
C'est en s'appuyant sur ce constat que la communaute du Example-Based MDE (EBMDE) a commence a utiliser des exemples d'application pour alimenter la reformulation SBSE du probleme d'apprentissage de tache MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un barometre efficace pour evaluer l'aptitude d'une solution a resoudre une tache. Cette mesure a prouve etre un objectif semantique de choix pour guider la recherche metaheuristique de solutions.
Cependant, s'il est communement admis que la representativite des exemples a un impact sur la generalisabilite des solutions, l'etude de cet impact souffre d'un manque de consideration flagrant. Dans cette these, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une methodologie complete pour caracteriser et evaluer la relation qui existe entre la generalisabilite des solutions et deux proprietes importantes des exemples, leur taille et leur couverture.
Nous effectuons l'analyse empirique de ces deux proprietes et nous proposons un plan detaille pour une analyse plus approfondie du concept de representativite, ou d'autres representativites.",
- notes = "Suvervisor: Houari Sahraoui",
