Machine learning-based model categorization using textual and structural features

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dc.authorid0000-0002-0397-6282
dc.authorid0000-0002-5898-7464
dc.authorid0000-0002-5436-6070
dc.contributor.authorKhalilipour, Alirezaen_US
dc.contributor.authorBozyiÄŸit, Fatmaen_US
dc.contributor.authorUtku, Canen_US
dc.contributor.authorChallenger, Moharramen_US
dc.date.accessioned2022-11-04T14:18:19Z
dc.date.available2022-11-04T14:18:19Z
dc.date.issued2022-09-08
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractModel 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%.en_US
dc.identifier.citationKhalilipour, A., BozyiÄŸit, 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_39en_US
dc.identifier.doi10.1007/978-3-031-15743-1_39
dc.identifier.endpage436
dc.identifier.isbn9783031157424
dc.identifier.isbn9783031157431
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.scopus2-s2.0-85137989554
dc.identifier.scopusqualityQ4
dc.identifier.startpage425
dc.identifier.urihttps://hdl.handle.net/11729/5110
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-15743-1_39
dc.identifier.volume1652
dc.identifier.wosWOS:000892609000038
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorUtku, Canen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectMetamodelen_US
dc.subjectModel driven engineeringen_US
dc.subjectModel managementen_US
dc.subjectText miningen_US
dc.subjectClassification (of information)en_US
dc.subjectLearning algorithmsen_US
dc.subjectLife cycleen_US
dc.subjectText processingen_US
dc.subjectMachine-learningen_US
dc.subjectModel classificationen_US
dc.subjectModel-driven engineeringen_US
dc.subjectStructural featureen_US
dc.subjectStructural informationen_US
dc.subjectText-miningen_US
dc.subjectTextual featuresen_US
dc.subjectTextual informationen_US
dc.subjectSoftwareen_US
dc.subjectSoftware maintenanceen_US
dc.subjectSoftware development processen_US
dc.titleMachine learning-based model categorization using textual and structural featuresen_US
dc.typeConference Objecten_US

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