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Automatic classific...
Automatic classification of UML Class diagrams from images
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- Ho-Quang, Truong (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Chaudron, Michel, 1969 (författare)
- Göteborgs universitet,University of Gothenburg
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- Samúelsson, Ingimar (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
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- Hjaltason, Jóel (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
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- Karasneh, B. (författare)
- Universiteit Leiden (UL),Leiden University (UL)
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- Osman, H. (författare)
- Universiteit Leiden (UL),Leiden University (UL)
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(creator_code:org_t)
- ISBN 9781479974252
- 2014
- 2014
- Engelska.
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Ingår i: Proceedings of the 21st Asia-Pacific Software Engineering Conference, APSEC 2014. - 1530-1362. - 9781479974252
- Relaterad länk:
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http://dx.doi.org/10...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- - Graphical modelling of various aspects of software and systems is a common part of software development. UML is the de-facto standard for various types of software models. To be able to research UML, academia needs to have a corpus of UML models. For building such a database, an automated system that has the ability to classify UML class diagram images would be very beneficial, since a large portion of UML class diagrams (UML CDs) is available as images on the Internet. In this study, we propose 23 image-features and investigate the use of these features for the purpose of classifying UML CD images. We analyse the performance of the features and assess their contribution based on their Information Gain Attribute Evaluation scores. We study specificity and sensitivity scores of six classification algorithms on a set of 1300 images. We found that 19 out of 23 introduced features can be considered as influential predictors for classifying UML CD images. Through the six algorithms, the prediction rate achieves nearly 96% correctness for UML-CD and 91% of correctness for non-UML CD.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Nyckelord
- Classification
- Feature extraction
- Machine learning
- Software Engineering
- UML
- UML class diagram
- Software Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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