一句话：提出无监督方法 Ockham，利用频繁子图挖掘从模型历史中学习能压缩模型差异的领域特定编辑操作。
模型转换在模型驱动软件开发中至关重要，但已有从元模型或示例推导转换的方法需大量手工、记录示例，或难以得到复杂转换。本文提出无监督方法 Ockham：假设有意义的领域特定编辑操作应能压缩模型差异，因此从模型仓库的历史中构建模型差异图，并用频繁子图挖掘发现其中的频繁结构，进而学习编辑操作。作者通过两个受控实验和一个铁路领域大规模工业模型驱动架构项目案例评估。结果表明，Ockham 能发现实际应用过的频繁编辑操作，并能提取工业实践中对开发者有意义的编辑操作。
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 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-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference 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 to practitioners in an industrial setting.
DOI 原文 ·
@inproceedings{paperbot6501,
title = {Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining},
author = {Christof Tinnes and Timo Kehrer and Mitchell Joblin and Uwe Hohenstein and Andreas Biesdorf and Sven Apel},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678698}
}