Assessing the quality of model-comparison tools

Brand, Mark van den; Hofkamp, A.T.; Verhoeff, T Tom; Protic, Z Zvezdan

doi:10.1145/2000410.2000412

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Furthermore, we also plan to investigate techniques to attach user-specified resolution strategies to signifier specifications (e.g., using the Epsilon Merging Language [EPK06]). Another line of future research is the evaluation of an signifier enhanced version of a model comparison framework with respect to precision and recall using a benchmark as proposed by van den Brand et al [vdBHVP11]. Finally, we plan to investigate the usefulness of signifiers in other fields, e.g., to detect equivalence correspondences between models in two-way merge scenarios [CNS12] such as model composition and weaving or to reason about the identities of model elements in bi-directional transformations [HLR08].…”

Section: Discussionmentioning

confidence: 99%

Language-Specific Model Versioning Based on Signifiers.

Langer¹,

Wimmer²,

Gray³

et al. 2012

JOT

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In model-driven engineering (MDE), models constitute the central artifacts in the development process, and thus, are often built by teams of developers. As a consequence, adequate version control for models is crucial to the success of MDE-based projects. Several model versioning systems have been proposed recently. Most of them are generic in the sense that they are agnostic to modeling languages. Although this ensures a wide applicability, important merge issues may not be detected.In this paper, we present an orthogonal extension to generic model versioning systems for enabling the detection of an important subset of language-specific merge issues. Users may enhance the versioning system's capabilities by defining signifiers, which describe the combination of features of a model element type that convey the superior meaning of its instances. Signifiers improve the different phases of the versioning process including comparing and merging models leading to a higher quality of the finally merged models. We showcase the applicability of our approach by enhancing the versioning support for the modeling language Ecore.

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Section: Discussionmentioning

confidence: 99%

Language-Specific Model Versioning Based on Signifiers.

Langer¹,

Wimmer²,

Gray³

et al. 2012

JOT

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“…Model Mutation Framework We use the model mutation framework in (van den Brand et al, 2011) to synthetically generate populations of models to be used for comparison. The framework is capable of generating mutations on a model based on its metamodel, i.e.…”

Section: A Dataset For Model Comparisonmentioning

confidence: 99%

“…It can be configured to generate random distribution of different types of mutations such as addition, removal or modification of model elements. For more information on the features and operation, please refer to (van den Brand et al, 2011).…”

Section: A Dataset For Model Comparisonmentioning

confidence: 99%

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Towards Statistical Comparison and Analysis of Models

Babur

Cleophas

Verhoeff

et al. 2016

Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development

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Systematic review of matching techniques used in model-driven methodologies

Somogyi

Asztalos

2019

Softw Syst Model

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In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling. Keywords Model matching • Model comparison • Model differencing • Version control • Text-based modeling • Systematic literature review Communicated by Professor Jon Whittle.

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Assessing the quality of model-comparison tools

Cited by 5 publications

References 11 publications

Language-Specific Model Versioning Based on Signifiers.

Language-Specific Model Versioning Based on Signifiers.

Towards Statistical Comparison and Analysis of Models

Systematic review of matching techniques used in model-driven methodologies

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