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Enhancing model transformation synthesis using natural language processing

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

Kevin Lano, Aminu Umar

Original languageEnglish
Title of host publicationProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
Pages277-286
Number of pages10
ISBN (Electronic)9781450381352
DOIs
Published16 Oct 2020
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Publication series

NameProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings

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  • mtreai

    mtreai.pdf, 701 KB, application/pdf

    Uploaded date:06 Nov 2020

King's Authors

Abstract

In this paper we examine how model transformation specifications can be derived from requirements and examples, using a combination of natural language processing (NLP), machine learning (ML) and inductive logic programming (ILP) techniques, together with search-based software engineering (SBSE) for metamodel matching. The AI techniques are employed in order to improve the performance and accuracy of the base SBSE approach, and enable this to be used for a wider range of transformation cases.
We propose a specific approach for the co-use of the techniques, and evaluate this on a range of transformation examples from different sources.

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