Islem Baki

An evolutionary approach that, in addition to learn model transformation rules from examples, allows to capture implicit and explicit control over the transformation rules. The derivation of both transformation and control knowledge is performed through a heuristic search, i.e., a genetic programming algorithm, guided by the conformance with examples of past transformations supplied as pairs of source and target models. Our approach is evaluated on four model transformation problems that require non-trivial control. The obtained results are convincing for three of the four studied problems.

We propose a Model Transformation By Example process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation. Our process relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas precision and recall values larger than 90% are recorded for the four remaining cases.