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Long Method Detection Using Graph Convolutional Networks
https://ipsj.ixsq.nii.ac.jp/records/227250
https://ipsj.ixsq.nii.ac.jp/records/2272507f8b3a8f-f138-41d0-9cca-4326c126515d
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-JNL6408003.pdf (1.2 MB)
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Copyright (c) 2023 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2023-08-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Long Method Detection Using Graph Convolutional Networks | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Long Method Detection Using Graph Convolutional Networks | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] Long Method, code smell, software refactoring, deep learning, graph convolutional networks | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Inner Mongolia University of Science & Technology | ||||||||||
| 著者所属 | ||||||||||
| Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Inner Mongolia University of Science & Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Waseda University | ||||||||||
| 著者名 |
HanYu, Zhang
× HanYu, Zhang
× Tomoji, Kishi
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| 著者名(英) |
HanYu, Zhang
× HanYu, Zhang
× Tomoji, Kishi
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Long Method is a code smell that frequently happens in software development, which refers to the complex method with multiple functions. Detecting and refactoring such problems has been a popular topic in software refactoring, and many detection approaches have been proposed. In past years, the approaches based on metrics or rules have been the leading way in long method detection. However, the approach based on deep learning has also attracted extensive attention in recent studies. In this paper, we propose a graph-based deep learning approach to detect Long Method. The key point of our approach is that we extended the PDG (Program Dependency Graph) into a Directed-Heterogeneous Graph as the input graph and used the GCN (Graph Convolutional Network) to build a graph neural network for Long Method detection. Moreover, to get substantial data samples for the deep learning task, we propose a novel semi-automatic approach to generate a large number of data samples. Finally, to prove the validity of our approach, we compared our approach with the existing approaches based on five groups of datasets manually reviewed. The evaluation result shows that our approach achieved a good performance in Long Method detection. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.31(2023) (online) DOI http://dx.doi.org/10.2197/ipsjjip.31.469 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Long Method is a code smell that frequently happens in software development, which refers to the complex method with multiple functions. Detecting and refactoring such problems has been a popular topic in software refactoring, and many detection approaches have been proposed. In past years, the approaches based on metrics or rules have been the leading way in long method detection. However, the approach based on deep learning has also attracted extensive attention in recent studies. In this paper, we propose a graph-based deep learning approach to detect Long Method. The key point of our approach is that we extended the PDG (Program Dependency Graph) into a Directed-Heterogeneous Graph as the input graph and used the GCN (Graph Convolutional Network) to build a graph neural network for Long Method detection. Moreover, to get substantial data samples for the deep learning task, we propose a novel semi-automatic approach to generate a large number of data samples. Finally, to prove the validity of our approach, we compared our approach with the existing approaches based on five groups of datasets manually reviewed. The evaluation result shows that our approach achieved a good performance in Long Method detection. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.31(2023) (online) DOI http://dx.doi.org/10.2197/ipsjjip.31.469 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 64, 号 8, 発行日 2023-08-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
