1 research outputs found
Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling
Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)
automatic approaches to derive model transformations from meta-models or from
examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex
transformations. We propose a novel unsupervised approach, called Ockham, which
is able to learn edit operations from model histories in model repositories. Ockham
is based on the idea that meaningful domain-specifc edit operations are the ones
that compress the model diferences. It employs frequent subgraph mining to discover frequent structures in model diference graphs. We evaluate our approach in
two controlled experiments and one real-world case study of a large-scale industrial
model-driven architecture project in the railway domain. We found that our approach
is able to discover frequent edit operations that have actually been applied before.
Furthermore, Ockham is able to extract edit operations that are meaningful—in the
sense of explaining model diferences through the edit operations they comprise—to
practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model diferences and change profles) for the discovered edit operations in
this industrial setting. We fnd that the edit operations discovered by Ockham can be
used to better understand and simulate the evolution of models