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

Automatic Inference of Rule-Based Specifications of Complex In-Place Model Transformations

Optimal support for continuous evolution in model-based software development requires tool environments to be customisable to domain-specific modelling languages. An important aspect is the set of change operations available to modify models. In-place model transformations are well-suited for that purpose. However, the specification of transformation rules requires a deep understanding of the language meta-model, limiting it to expert tool developers and language designers. This is at odds with the aim of domain-specific visual modelling environments, which should be customisable by domain experts. We follow a model transformation by-example approach to mitigate that problem: Users generate transformation rules by creating examples of transformations using standard visual editors as macro recorders. Our ambition is to stick entirely to the concrete visual notation domain experts are familiar with, using rule inference to generalise a set of transformation examples. In contrast to previous approaches to the same problem, our approach supports the inference of complex rule features such as negative application conditions, multi-object patterns and global invariants. We illustrate the functioning of our approach by the inference of a complex and widely used refactoring operation on UML class diagrams