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

arXiv: https://arxiv.org/abs/2108.01001 · HTML: https://mlsanity.com/paper/2108.01001/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets](https://mlsanity.com/paper/2406.17651/) (2024)
- [Towards Automatic Support of Software Model Evolution with Large Language~Models](https://mlsanity.com/paper/2312.12404/) (2023)

## Abstract (arXiv)

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.