Using n-grams for the Automated Clustering of Structural Models

Babur, Önder; Cleophas, Loek

doi:10.1007/978-3-319-51963-0_40

Cited by 22 publications

(19 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features can be, for instance, singleton names of model elements (very similar to the vocabulary of documents) or larger fragments of the underlying graph structure such as ngrams [30]. In our context, an example n-gram for a EMF metamodel would be for = n 2 an EClass containing an EAttribute [31]. SAMOS computes a term-frequency based VSM, using comparison schemes (for instance determining whether to match metaclasses or ignore them), weighting schemes (for instance EClass weighted higher than EAttribute) and NLP such as stemming and synonym checking.…”

Section: Using and Extending Samos For Clone Detectionmentioning

confidence: 99%

“…Our extended version of SAMOS, supporting subtree extraction in addition to the previously existing options [31], has the following three feature settings:…”

Section: Encoding Structure In N-grams and Subtreesmentioning

confidence: 99%

“…Based on the graph representation of a model, we can describe n-grams as n consecutively connected vertices. In contrast to [31] which simply omitted the edge information, we also incorporate the edges in the n-grams as SimpleTypes in order to represent the edge information as well. Although it is quite intuitive, the readers are referred to [31] for more details on the graph traversal and n-gram extraction.…”

Section: Encoding Structure In N-grams and Subtreesmentioning

confidence: 99%

“…by instead using a reducing multiplier of 0.5 for non-matching types, ignoring attributes altogether, and so on. N-gram comparison is the semi-relaxed formula [31]; i.e. given n-grams v 1 and v 2 with n 2 1 elements (n vertices and n 1 edges corresponding to v n 1 1..2 1 and v n 2 1..2 1 ), the n-gram similarity is:…”

Section: Vertex and N-gram Comparisonmentioning

confidence: 99%

See 3 more Smart Citations

Metamodel clone detection with SAMOS

Babur

Cleophas

Brand

2019

Journal of Computer Languages

Self Cite

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

Section: Using and Extending Samos For Clone Detectionmentioning

confidence: 99%

“…Our extended version of SAMOS, supporting subtree extraction in addition to the previously existing options [31], has the following three feature settings:…”

Section: Encoding Structure In N-grams and Subtreesmentioning

confidence: 99%

Section: Encoding Structure In N-grams and Subtreesmentioning

confidence: 99%

Section: Vertex and N-gram Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Metamodel clone detection with SAMOS

Babur

Cleophas

Brand

2019

Journal of Computer Languages

Self Cite

View full text Add to dashboard Cite

show abstract

“…They translate models into a vector space representation to reuse existing generic clustering distance measures. In follow up work [BC17,BCvdB18], the authors have proposed n-grams for clustering of models, again based on the usage of generic distance metrics. In contrast, we propose the usage of domain-specific distance metrics that are directly measured on the model structures.…”

Section: Model Clusteringmentioning

confidence: 99%

Domain-Specific Model Distance Measures.

Syriani¹,

Bill²,

Wimmer³

2019

JOT

View full text Add to dashboard Cite

Much research was invested in the last decade to develop differencing methods to identify the changes performed between two model versions. Typically, these changes are captured in an explicit difference model. However, quantifying the distance between model versions received less attention. While different versions of a model may have the same amount of changes, their distance to the base model may be drastically different. Therefore, we present distance metrics for models. We provide a method to generate tool support for computing domainspecific distance measures automatically. We show the benefits of distance measures over model differences in the use case of searching for the explanation of model evolution in terms of domain-specific change operations. The results of our experiments show that using distance metrics outperforms the usage of common difference models.

show abstract