Tackling Model Drifts in Industrial Model-driven Software Product Lines by Means of a Graph Database

Christof Tinnes, Uwe Hohenstein, Wolfgang Rössler, Andreas Biesdorf

2022

Abstract

This paper reports on our experience of using a graph database to efficiently compare very large models in an industrial model-driven engineering project. The need for a comparison results from the fact that architectural models are reused. They conform to a common domain-specific language but diverge as they belong to different products managed in separate branches of a repository in the sense of a clone-and-own approach. In the presented industry project, huge models are developed and reside in the commercial tool MAGICDRAW. In fact, unlike many other tools, MAGICDRAW turned out to be capable to handle those huge models in industrial environments. In this context, there is a strong necessity to detect and judge relevant differences between models in different branches in order to avoid a model drift and loosing reuse opportunities across the products. Indeed, MAGICDRAW has a built-in difference tool, which however exposes an excessive number of differences, only a fraction of which are really relevant for certain tasks. We show that the capabilities of the graph database NEO4J can be leveraged to reduce the differences to relevant ones. The expressiveness of NEO4J turned out to be sufficient, just as the performance did.

Paper Citation

in Harvard Style

Tinnes C., Hohenstein U., Rössler W. and Biesdorf A. (2022). Tackling Model Drifts in Industrial Model-driven Software Product Lines by Means of a Graph Database. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 146-157. DOI: 10.5220/0011319800003269

in Bibtex Style

@conference{data22,
author={Christof Tinnes and Uwe Hohenstein and Wolfgang Rössler and Andreas Biesdorf},
title={Tackling Model Drifts in Industrial Model-driven Software Product Lines by Means of a Graph Database},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={146-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011319800003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Tackling Model Drifts in Industrial Model-driven Software Product Lines by Means of a Graph Database
SN - 978-989-758-583-8
AU - Tinnes C.
AU - Hohenstein U.
AU - Rössler W.
AU - Biesdorf A.
PY - 2022
SP - 146
EP - 157
DO - 10.5220/0011319800003269