An LSTM-Based Neural Network Architecture for Model Transformations

• Published in: 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS) pp. 294 - 299
• Main Authors: Burgueno, Loli, Cabot, Jordi, Gerard, Sebastien
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2019
• Subjects: LSTM ANN; MDE; model transformations
• DOI: 10.1109/MODELS.2019.00013

Model transformations are a key element in any model-driven engineering approach. But writing them is a time-consuming and error-prone activity that requires specific knowledge of the transformation language semantics. 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. Once the transformation mappings have been learned, the LSTM system is able to autonomously transform new input models into their corresponding output models without the need of writing any transformation-specific code. We evaluate the correctness and performance of our approach and discuss its advantages and limitations.