English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Model transformations play a fundamental role in model-driven softwaredevelopment. They can be used to solve or support central tasks, such ascreating models, handling model co-evolution, and model merging. In the past,various (semi-)automatic approaches have been proposed to derive modeltransformations from meta-models or from examples. These approaches requiretime-consuming handcrafting or recording of concrete examples, or they areunable to derive complex transformations. We propose a novel unsupervisedapproach, called Ockham, which is able to learn edit operations from modelhistories in model repositories. Ockham is based on the idea that meaningfuledit operations will be the ones that compress the model differences. Weevaluate our approach in two controlled experiments and one real-world casestudy of a large-scale industrial model-driven architecture project in therailway domain. We find that our approach is able to discover frequent editoperations that have actually been applied. Furthermore, Ockham is able toextract edit operations in an industrial setting that are meaningful topractitioners.

Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining