We propose to take advantage of the advances in Artificial Intelligence and, in particular, Long Short-Term Memory Neural Networks (LSTM), to automatically infer model transformations from sets of input-output model pairs.
Summary of our contributions towards a scalable query and transformation modeling framework able to handle very large models
Overcome the limitations of executing model transformations on large models by distributing them on top of a mapreduce infrastructure. We’ve done it for ATL. Read the full details on this paper accepted in the Journal of Systems and Software
IDEs for model transformation languages are still rudimentary compared to IDEs of languages like Java o C. AnATLyzer has been created to fill this gap and provide some advanced IDE features for ATL.
Gremlin-ATL is a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases
