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, 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.