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    Categorization of the models based on structural information extraction and machine learning
    (Springer Science and Business Media Deutschland GmbH, 2022-07-21) Khalilipour, Alireza; Bozyiğit, Fatma; Utku, Can; Challenger, Moharram
    As various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with state of the art very expensive computationally, i.e., leads to non-tractable graph comparison algorithms. To handle this problem, modelers can organize available models to be reused and overcome the development of the new and more complex models with less cost and effort. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels. In our proposed system, the structural information of each model was summarized in its elements through generating their simple labelled graphs. The proposed solution is to transform the complex attributed graphs of the models to simply labelled graphs so that graph analysis algorithms can be applied to them. The labelled graphs (models) were structurally compared using graph comparison techniques such as graph kernels, and the results were used as a set of features for similarity search. After generating feature vectors, the performance of six machine learning classifiers (Naïve Bayes (NB), k Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) were evaluated on the feature vectors. The presented model yields promising results for the model classification task with a classification accuracy over 87%.
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    Machine learning-based model categorization using textual and structural features
    (Springer Science and Business Media Deutschland GmbH, 2022-09-08) Khalilipour, Alireza; Bozyiğit, Fatma; Utku, Can; Challenger, Moharram
    Model Driven Engineering (MDE), where models are the core elements in the entire life cycle from the specification to maintenance phases, is one of the promising techniques to provide abstraction and automation. However, model management is another challenging issue due to the increasing number of models, their size, and their structural complexity. So that the available models should be organized by modelers to be reused and overcome the development of the new and more complex models with less cost and effort. In this direction, many studies are conducted to categorize models automatically. However, most of the studies focus either on the textual data or structural information in the intelligent model management, leading to less precision in the model management activities. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels through hybrid feature vectors including both textual and structural information. In the proposed approach, first, the textual information of each model has been summarized in its elements through text processing as well as the ontology of synonyms within a specific domain. Then, the performances of machine learning classifiers were observed on two different variants of the datasets. The first variant includes only textual features (represented both in TF-IDF and word2vec representations), whereas the second variant consists of the determined structural features and textual features. It was finally concluded that each experimented machine learning algorithm gave more successful prediction performance on the variant containing structural features. The presented model yields promising results for the model classification task with a classification accuracy of 89.16%.