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

TL;DR

This paper introduces Ockham, an unsupervised method that learns meaningful domain-specific model edit operations from repositories by identifying those that best compress model differences, demonstrated through experiments and an industrial case study.

Contribution

The paper presents a novel unsupervised approach, Ockham, for extracting meaningful model edit operations from repositories using frequent subgraph mining, addressing limitations of prior methods.

Findings

Ockham successfully discovers frequent edit operations used in practice.

It extracts meaningful edit operations in an industrial setting.

The approach is validated through experiments and a real-world case study.

Abstract

Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or 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 edit operations will be the ones that compress the model differences. 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 find that our approach is able to discover frequent edit operations that have actually been applied. Furthermore, Ockham is able to extract edit operations in an industrial setting that are meaningful to practitioners.