Graph neural networks for precise bug localization through structural program analysis

Abstract Bug localization (BL) is known as one of the major steps in the program repair process, which generally seeks to find a set of commands causing a program to crash or fail. At the present time, locating bugs and their sources quickly seems to be impossible as the complexity of modern software development and scaling is soaring. Accordingly, there is a huge demand for BL techniques with minimal human intervention. A graph representing source code typically encodes valuable information about both the syntactic and semantic structures of programs. Many software bugs are associated with these structures, making graphs particularly suitable for bug localization (BL). Therefore, the key contributions of this work involve labeling graph nodes, classifying these nodes, and addressing imbalanced classifications within the graph data structure to effectively locate bugs in code. A graph-based bug classifier is initially introduced in the method proposed in this paper. For this purpose, the program source codes are mapped to a graph representation. Since the graph nodes do not have labels, the Gumtree algorithm is then exploited to label them by comparing the buggy graphs and the corresponding bug-free ones. Afterward, a trained, supervised node classifier, developed based on a graph neural network (GNN), is applied to classify the nodes into buggy or bug-free ones. Given the imbalance in the data, accuracy, precision, recall, and F1-score metrics are used for evaluation. Experimental results on identical datasets show that the proposed method outperforms other related approaches. The proposed approach effectively localizes a broader spectrum of bug types, such as undefined properties, functional bugs, variable naming errors, and variable misuse issues .