{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:23:18Z","timestamp":1725754998025},"reference-count":48,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2024,9,1]]},"DOI":"10.1587\/transinf.2023edp7192","type":"journal-article","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T22:19:19Z","timestamp":1725142759000},"page":"1140-1150","source":"Crossref","is-referenced-by-count":0,"title":["Large Class Detection Using GNNs: A Graph Based Deep Learning Approach Utilizing Three Typical GNN Model Architectures"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"HanYu","family":"ZHANG","sequence":"first","affiliation":[{"name":"Inner Mongolia University of Science &amp; Technology"}]},{"given":"Tomoji","family":"KISHI","sequence":"additional","affiliation":[{"name":"Waseda University"}]}],"member":"532","reference":[{"unstructured":"[1] W.F. Opdyke, \u201cRefactoring Object-Oriented Frameworks,\u201d PhD thesis, University of Illinois at Urbana, 1992.","key":"1"},{"unstructured":"[2] M. Fowler, ed., Refactoring: improving the design of existing code, Addison-Wesley Signature, 1999 1st ed, 2018 2nd ed.","key":"2"},{"doi-asserted-by":"publisher","unstructured":"[3] T. Sharma and D. Spinellis, \u201cA survey on software smells,\u201d Journal of Systems and Software, vol.138, pp.158-173, 2018. 10.1016\/j.jss.2017.12.034","key":"3","DOI":"10.1016\/j.jss.2017.12.034"},{"doi-asserted-by":"publisher","unstructured":"[4] A. AbuHassan, M. Alshayeb, and L. Ghouti, \u201cSoftware smell detection techniques: A systematic literature review,\u201d Journal of Software: Evolution and Process, vol.33, no.3, e2320, 2021. 10.1002\/smr.2320","key":"4","DOI":"10.1002\/smr.2320"},{"unstructured":"[5] W.J. Brown, R.C. Malveau, H.W. McCormick, T.J. Mowbray, ed., Anti Patterns: Refactoring software, architectures, and projects in crisis, John Wiley and Sons, 1998.","key":"5"},{"unstructured":"[6] M. Lanza, R. Marinescu, ed., Object-Oriented Metrics in Practice, 2006.","key":"6"},{"doi-asserted-by":"publisher","unstructured":"[7] F.A. Fontana, M.V. M\u00e4ntyl\u00e4, M. Zanoni, and A. Marino, \u201cComparing and experimenting machine learning techniques for code smell detection,\u201d Empirical Software Engineering, vol.21, no.3, pp.1143-1191, 2016. 10.1007\/s10664-015-9378-4","key":"7","DOI":"10.1007\/s10664-015-9378-4"},{"doi-asserted-by":"publisher","unstructured":"[8] H. Liu, J. Jin, Z. Xu, Y. Zou, Y. Bu, and L. Zhang, \u201cDeep Learning Based Code Smell Detection,\u201d IEEE Trans. Softw. Eng., vol.47, no.9, pp.1811-1837, 2021. 10.1109\/tse.2019.2936376","key":"8","DOI":"10.1109\/TSE.2019.2936376"},{"doi-asserted-by":"crossref","unstructured":"[9] N. Moha, Y.-G. Gueheneuc, L. Duchien, and A.-F. Le Meur, \u201cDECOR: A Method for the Specification and Detection of Code and Design Smells,\u201d IEEE Trans. Softw. Eng., vol.36, no.1, pp.20-36, 2010. 10.1109\/tse.2009.50","key":"9","DOI":"10.1109\/TSE.2009.50"},{"doi-asserted-by":"crossref","unstructured":"[10] M. Fokaefs, N. Tsantalis, E. Stroulia, and A. Chatzigeorgiou, \u201cJDeodorant: identification and application of extract class refactorings,\u201d ICSE &apos;11: Proc. 33rd International Conference on Software Engineering, pp.1037-1039, 2011. 10.1145\/1985793.1985989","key":"10","DOI":"10.1145\/1985793.1985989"},{"doi-asserted-by":"publisher","unstructured":"[11] M. Fokaefs, N. Tsantalis, E. Stroulia, and A. Chatzigeorgiou, \u201cIdentification and application of Extract Class refactorings in object-oriented systems,\u201d Journal of Systems and Software, vol.85, no.10, pp.2241-2260, 2012. 10.1016\/j.jss.2012.04.013","key":"11","DOI":"10.1016\/j.jss.2012.04.013"},{"doi-asserted-by":"crossref","unstructured":"[12] P.S. Akash, A.Z. Sadiq, and A. Kabir, \u201cAn Approach of Extracting God Class Exploiting Both Structural and Semantic Similarity,\u201d ENASE 2019, pp.427-433, 2019. 10.5220\/0007743804270433","key":"12","DOI":"10.5220\/0007743804270433"},{"doi-asserted-by":"crossref","unstructured":"[13] S. Charalampidou, A. Ampatzoglou, and P. Avgeriou, \u201cSize and cohesion metrics as indicators of the long method bad smell: An empirical study,\u201d PROMISE &apos;15, vol.8, pp.1-10, 2015. 10.1145\/2810146.2810155","key":"13","DOI":"10.1145\/2810146.2810155"},{"doi-asserted-by":"publisher","unstructured":"[14] J. Bansiya and C.G. Davis, \u201cA hierarchical model for object-oriented design quality assessment,\u201d IEEE Trans. Softw. Eng., vol.28, no.1, pp.4-17, 2002. 10.1109\/32.979986","key":"14","DOI":"10.1109\/32.979986"},{"doi-asserted-by":"publisher","unstructured":"[15] S.R. Chidamber and C.F. Kemerer, \u201cA metrics suite for object oriented design,\u201d IEEE Trans. Softw. Eng., vol.20, no.6, pp.476-493, 1994. 10.1109\/32.295895","key":"15","DOI":"10.1109\/32.295895"},{"doi-asserted-by":"crossref","unstructured":"[16] G. Bavota, A.D. Lucia, A. Marcus, and R. Oliveto, \u201cA two-step technique for extract class refactoring,\u201d ASE &apos;10, vol.20, pp.151-154, 2010. 10.1145\/1858996.1859024","key":"16","DOI":"10.1145\/1858996.1859024"},{"doi-asserted-by":"crossref","unstructured":"[17] G. Gui and P.D. Scott, \u201cCoupling and cohesion measures for evaluation of component reusability,\u201d MSR &apos;06, pp.18-21, 2006. 10.1145\/1137983.1137989","key":"17","DOI":"10.1145\/1137983.1137989"},{"doi-asserted-by":"publisher","unstructured":"[18] D. Poshyvanyk, A. Marcus, R. Ferenc, and T. Gyim\u00f3thy, \u201cUsing information retrieval based coupling measures for impact analysis,\u201d Empirical Software Engineering, vol.14, no.1, pp.5-32, 2009. 10.1007\/s10664-008-9088-2","key":"18","DOI":"10.1007\/s10664-008-9088-2"},{"doi-asserted-by":"crossref","unstructured":"[19] S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman, \u201cIndexing by latent semantic analysis,\u201d Journal of the American Society for Information, vol.41, no.6, pp.391-407, 1990. 10.1002\/(sici)1097-4571(199009)41:6%3C391::aid-asi1%3E3.0.co;2-9","key":"19","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"unstructured":"[20] D.M. Blei, A.Y. Ng, and M.I. Jordan, \u201cLatent dirichlet allocation,\u201d Journal of Machine Learning Research, vol.3, pp.993-1022, 2003. 10.7551\/mitpress\/1120.003.0082","key":"20"},{"unstructured":"[21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d Journal of Machine Learning Research, arXiv:1301.3781 [cs.CL].","key":"21"},{"unstructured":"[22] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cDistributed representations of words and phrases and their compositionality,\u201d Proc. 26th International Conference on Neural Information Processing Systems, vol.2, pp.3111-3119, 2013.","key":"22"},{"doi-asserted-by":"publisher","unstructured":"[23] J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, \u201cGraph neural networks: A review of methods and applications,\u201d AI Open, vol.1, pp.57-81, 2020. 10.1016\/j.aiopen.2021.01.001","key":"23","DOI":"10.1016\/j.aiopen.2021.01.001"},{"unstructured":"[24] M. Gori, G. Monfardini, and F. Scarselli, \u201cA new model for learning in graph domains,\u201d IEEE International Joint Conference on Neural Networks, 2005.","key":"24"},{"doi-asserted-by":"publisher","unstructured":"[25] F. Scarselli, M. Gori, M. Hagenbuchner, and G. Monfardini, \u201cThe graph neural network model,\u201d IEEE Trans. Neural Netw., vol.20, no.1, pp.61-80, 2009. 10.1109\/tnn.2008.2005605","key":"25","DOI":"10.1109\/TNN.2008.2005605"},{"unstructured":"[26] W.L. Hamilton, R. Ying, and J. Leskovec, \u201cInductive representation learning on large graphs,\u201d NIPS&apos;17, pp.1024-1034, 2017.","key":"26"},{"unstructured":"[27] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d ICLR&apos;2017, 2017.","key":"27"},{"unstructured":"[28] P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2, and Y. Bengio, \u201cGraph Attention Networks,\u201d ICLR 2018, 2018.","key":"28"},{"doi-asserted-by":"publisher","unstructured":"[29] H.Y. Zhang and T. Kishi, \u201cLong Method Detection using Graph Convolutional Networks,\u201d Journal of Information Process, vol.31, pp.469-477, 2023. 10.2197\/ipsjjip.31.469","key":"29","DOI":"10.2197\/ipsjjip.31.469"},{"doi-asserted-by":"publisher","unstructured":"[30] B. Khemani, S. Patil, K. Kotecha, and S. Tanwar, \u201cA review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions,\u201d Journal of Big Data, vol.11, no.1, 2024. 10.1186\/s40537-023-00876-4","key":"30","DOI":"10.1186\/s40537-023-00876-4"},{"doi-asserted-by":"crossref","unstructured":"[31] Q. McNemar, \u201cNote on the sampling error of the difference between correlated proportions or percentages,\u201d Psychometrika, vol.12, no.2, pp.153-157, 1947. 10.1007\/bf02295996","key":"31","DOI":"10.1007\/BF02295996"},{"unstructured":"[32] S. Raschka, \u201cModel evaluation, model selection, and algorithm selection in machine learning,\u201d arXiv:1811.12808 [cs.LG].","key":"32"},{"unstructured":"[33] Junit, https:\/\/github.com\/junit-team\/junit4, accessed Nov. 29 2022.","key":"33"},{"unstructured":"[34] Mybatis, https:\/\/github.com\/mybatis\/mybatis-3, accessed Nov. 29 2022.","key":"34"},{"unstructured":"[35] JEdit, http:\/\/www.jedit.org\/index.php?page=download, accessed Nov. 29 2022.","key":"35"},{"doi-asserted-by":"crossref","unstructured":"[36] Netty, https:\/\/netty.io\/, accessed Nov. 29 2022.","key":"36","DOI":"10.55606\/corammundo.v4i2.59"},{"unstructured":"[37] PMD, https:\/\/github.com\/pmd\/pmd, accessed Nov. 29 2022.","key":"37"},{"unstructured":"[38] Gephi, https:\/\/gephi.org\/, accessed Nov. 29 2022.","key":"38"},{"unstructured":"[39] Libgdx, https:\/\/libgdx.com\/, accessed Nov. 29 2022.","key":"39"},{"unstructured":"[40] RxJava, https:\/\/github.com\/ReactiveX\/RxJava, accessed Nov. 29 2022.","key":"40"},{"unstructured":"[41] tree-sitter, https:\/\/github.com\/tree-sitter\/, accessed Nov. 29 2022.","key":"41"},{"unstructured":"[42] OpenRefine, https:\/\/github.com\/OpenRefine\/, accessed Nov. 29 2022.","key":"42"},{"unstructured":"[43] Jgrapht, https:\/\/jgrapht.org\/, accessed Nov. 29 2022.","key":"43"},{"unstructured":"[44] Freeplane, https:\/\/github.com\/freeplane\/, accessed Nov. 29 2022.","key":"44"},{"unstructured":"[45] Open Hospital, https:\/\/github.com\/informatici, accessed Nov. 29 2022.","key":"45"},{"unstructured":"[46] Jsprit, https:\/\/github.com\/graphhopper\/jsprit, accessed Nov. 29 2022.","key":"46"},{"unstructured":"[47] PyTorch, https:\/\/pytorch.org\/, accessed Nov. 29 2022.","key":"47"},{"unstructured":"[48] DGL, https:\/\/www.dgl.ai\/, accessed Nov. 29 2022.","key":"48"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/9\/E107.D_2023EDP7192\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T04:19:37Z","timestamp":1725682777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/9\/E107.D_2023EDP7192\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7192","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2024,9,1]]},"article-number":"2023EDP7192"}}