Learning implicit and explicit control in model transformations by example

Abstract

We propose 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.