Enhancing model transformation synthesis using natural language processing
Kevin Lano, S. Fang, Muhammad Umar, Sobhan Yassipour-Tehrani · 2020
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.