Generating Examples for Knowledge Abstraction in MDE: a Multi-Objective Framework.
Pourquoi ce travail est-il dans la base ?
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Le tri à trois modèles
les 1 000 travaux triés →Les trois modèles l'ont jugé hors champ.
Model-driven software engineering method for generating model examples; a software technique, not a study of research.
The paper develops an automated model-generation framework for software engineering, not a study of research itself.
Software-engineering method for generating MDE model examples; SE tooling, not study of how research is done.
Résumé
Model-Driven Engineering (MDE) aims at raising the level of abstraction in software development and therefore relies on task automation. To foster automation, MDE promotes the use of specific domain languages (DSLs), essential to express ideas at the domain level. Furthermore, to ease communication between computer science and other fields, modelers employ model examples (i.e., selected metamodel instances) to illustrate and refine their conceptual ideas. But, if the use of model examples has shown its efficiency, it is still an ad hoc process which requires automation. In this paper, we briefly depict the thorough example-toknowledge learning process. Then, we present a framework that produces, from a metamodel, a representative model example set with regards to a given coverage definition. To find the best trade-off between coverage and a necessary minimality objectives, we use a non-dominated genetic algorithm (NSGAII). We illustrated our method by generating a near-optimal set of models for the peculiar constraint learning task. We evaluated its efficiency comparing the resulting generated set with the best one issued from a raw random generation. Our encouraging preliminary results let us envision a deep study of the relation between various types of coverage and their impact on our ability to abstract knowledge from examples.
Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.
La notice
- Revue
- Thématique
- Model-Driven Software Engineering Techniques
- Domaine
- Computer Science
- Établissements canadiens
- Université de Montréal
- Organismes subventionnaires
- —
- Mots-clés
- MetamodelingComputer scienceAbstractionSoftware engineeringAutomationModel-driven architectureTask (project management)Process (computing)Domain (mathematical analysis)Set (abstract data type)Abstraction layerProgramming languageArtificial intelligenceTheoretical computer scienceMachine learningSoftware developmentSoftwareSystems engineeringEngineering
- Résumé présent dans OpenAlex
- oui