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Type inference in flexible model-driven engineering using classification algorithms

Research output: Contribution to journalArticlepeer-review



Publication details

JournalInternational Journal on Software & Systems Modelling
DateAccepted/In press - 11 Jan 2018
DateE-pub ahead of print - 23 Jan 2018
DatePublished (current) - 8 Feb 2019
Issue number1
Number of pages23
Pages (from-to)345-366
Early online date23/01/18
Original languageEnglish


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.

    Research areas

  • Bottom-up metamodelling, Classification and regression trees, Flexible model-driven engineering, Model-driven engineering, Random forests, Type inference

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