Machine learning-based model categorization using textual and structural features
Yükleniyor...
Tarih
2022-09-08
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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%.
Açıklama
Anahtar Kelimeler
Machine learning, Metamodel, Model driven engineering, Model management, Text mining, Classification (of information), Learning algorithms, Life cycle, Text processing, Machine-learning, Model classification, Model-driven engineering, Structural feature, Structural information, Text-mining, Textual features, Textual information, Software, Software maintenance, Software development process
Kaynak
Communications in Computer and Information Science
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
1652
Sayı
Künye
Khalilipour, A., Bozyiğit, F., Utku, C. & Challenger, M. (2022). Machine learning-based model categorization using textual and structural features. Paper presented at the Communications in Computer and Information Science, 1652, 425-436. doi:10.1007/978-3-031-15743-1_39
