Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challen...

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Vydáno v:Software and systems modeling Ročník 18; číslo 1; s. 345 - 366
Hlavní autoři: Zolotas, Athanasios, Matragkas, Nicholas, Devlin, Sam, Kolovos, Dimitrios S., Paige, Richard F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019
Springer Nature B.V
Témata:
Algorithms
Classification
Compilers
Computer Science
Inference
Information Systems Applications (incl.Internet)
Interpreters
IT in Business
Knowledge management
Mathematical models
Metamodels
Programming Languages
Programming Techniques
Semantics
Software Engineering
Software Engineering/Programming and Operating Systems
Special Section Paper
Subject specialists
Model-driven engineering
Type inference
Flexible model-driven engineering
Bottom-up metamodelling
Classification and regression trees
Random forests
ISSN:1619-1366, 1619-1374
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Shrnutí:Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.
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ISSN:1619-1366
1619-1374
DOI:10.1007/s10270-018-0658-5