Type inference in flexible model-driven engineering using classification algorithms

• Published in: Software and systems modeling Vol. 18; no. 1; pp. 345 - 366
• Main Authors: Zolotas, Athanasios, Matragkas, Nicholas, Devlin, Sam, Kolovos, Dimitrios S., Paige, Richard F.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer Nature B.V
• Subjects: 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; 1619-1374
• DOI: 10.1007/s10270-018-0658-5

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.