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With model transformations arising to primary development artifacts in Model-Driven Engineering, dedicated tools supporting transformation developers in the development and maintenance of model transformations are strongly required. In this paper, we address the versioning of model transformations, which essentially relies on a basic service for comparing different versions of model transformations, e.g., a local workspace version and the latest version of a repository. Focusing on rule-based model transformations based on graph transformation concepts, we propose to compare such transformation rules using a maximum common subgraph (MCS) algorithm as the underlying matching engine. Although the MCS problem is known as a non-polynomial optimization problem, our research hypothesis is that using an MCS algorithm as a basis for comparing graph-based transformation rules is feasible for real-world model transformations and increases the quality of comparison results compared to standard model comparison algorithms. Experimental results obtained on a benchmark set for model transformation confirm this hypothesis.

In clone-and-own development, new variants of a software system are typically created by manually copying and adapting an existing variant. This approach is flexible but suffers from various challenges such as high maintenance cost in the long term. While researchers started to address the challenges of clone-and-own, there is yet little empirical evidence on the efficiency and effectiveness of clone-andown research. The main reason for this is the lack of appropriate benchmarks, which need to expose a multitude of different data and meta-data serving as input and ground truth for experimental evaluations. We present VEVOS, a benchmark generation framework that picks up these requirements and, given the version history of a software product line, enables the simulation of the evolution of cloned variants, and provides meta-data serving as ground truth. CCS CONCEPTS• Software and its engineering → Software configuration management and version control systems.

Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN   (Range Queries on $$\text {N}$$ N input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.