Automatic bug localization using a combination of deep learning and model transformation through node classification
Bug localization is the task of automatically locating suspicious commands in the source code. Many automated bug localization approaches have been proposed for reducing costs and speeding up the bug localization process. These approaches allow developers to focus on critical commands. In this paper...
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| Veröffentlicht in: | Software quality journal Jg. 31; H. 4; S. 1045 - 1063 |
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| ISSN: | 0963-9314, 1573-1367 |
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| Abstract | Bug localization is the task of automatically locating suspicious commands in the source code. Many automated bug localization approaches have been proposed for reducing costs and speeding up the bug localization process. These approaches allow developers to focus on critical commands. In this paper, we propose to treat the bug localization problem as a node classification problem. As in the existing training sets, where whole graphs are labeled as buggy and bug-free, it is required first to label all nodes in each graph. To do this, we use the Gumtree algorithm, which labels the nodes by comparing the buggy graphs with their corresponding fixed graphs. In classification, we propose to use a type of graph neural networks (GNNs), GraphSAGE. The used dataset for training and testing is JavaScript buggy code and their corresponding fixed code. The results demonstrate that the proposed method outperforms other related methods. |
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| AbstractList | Bug localization is the task of automatically locating suspicious commands in the source code. Many automated bug localization approaches have been proposed for reducing costs and speeding up the bug localization process. These approaches allow developers to focus on critical commands. In this paper, we propose to treat the bug localization problem as a node classification problem. As in the existing training sets, where whole graphs are labeled as buggy and bug-free, it is required first to label all nodes in each graph. To do this, we use the Gumtree algorithm, which labels the nodes by comparing the buggy graphs with their corresponding fixed graphs. In classification, we propose to use a type of graph neural networks (GNNs), GraphSAGE. The used dataset for training and testing is JavaScript buggy code and their corresponding fixed code. The results demonstrate that the proposed method outperforms other related methods. Bug localization is the task of automatically locating suspicious commands in the source code. Many automated bug localization approaches have been proposed for reducing costs and speeding up the bug localization process. These approaches allow developers to focus on critical commands. In this paper, we propose to treat the bug localization problem as a node classification problem. As in the existing training sets, where whole graphs are labeled as buggy and bug-free, it is required first to label all nodes in each graph. To do this, we use the Gumtree algorithm, which labels the nodes by comparing the buggy graphs with their corresponding fixed graphs. In classification, we propose to use a type of graph neural networks (GNNs), GraphSAGE. The used dataset for training and testing is JavaScript buggy code and their corresponding fixed code. The results demonstrate that the proposed method outperforms other related methods. |
| Author | Yousofvand, Leila Rafe, Vahid Soleimani, Seyfollah |
| Author_xml | – sequence: 1 givenname: Leila surname: Yousofvand fullname: Yousofvand, Leila organization: Department of Computer Engineering, Faculty of Engineering, Arak University – sequence: 2 givenname: Seyfollah surname: Soleimani fullname: Soleimani, Seyfollah email: s-soleimani@araku.ac.ir organization: Department of Computer Engineering, Faculty of Engineering, Arak University – sequence: 3 givenname: Vahid surname: Rafe fullname: Rafe, Vahid organization: Department of Computer Engineering, Faculty of Engineering, Arak University, Department of Computing, Goldsmiths University of London |
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| CitedBy_id | crossref_primary_10_1109_ACCESS_2025_3587202 crossref_primary_10_1109_TR_2024_3374410 crossref_primary_10_1007_s11432_023_4127_5 crossref_primary_10_1016_j_infsof_2025_107722 crossref_primary_10_1007_s10462_023_10686_y crossref_primary_10_1002_qre_70052 |
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