Enhancing bug localization with bug report decomposition and code hierarchical network

Bug localization, which aims to locate buggy source code files for given bug reports, is a crucial yet challenging software-mining task. Despite remarkable success, the state of the art falls short in handling (1) bug reports with diverse characteristics and (2) programs with wildly different behaviors. In response, this paper proposes a graph-based neural model BLOCO for automated bug localization. To be specific, our proposed model decomposes bug reports into several bug clues to capture bug-related information from various perspectives for highly diverse bug reports. To understand the program in depth, we first design a code hierarchical network structure, Code-NoN, based on basic blocks to represent source code files. Correspondingly, a multilayer graph neural network is tailored to capture program behaviors from the Code-NoN structure of each source code file. Finally, BLOCO further incorporates a bi-affine classifier to comprehensively predict the relationship between the bug reports and source files. Extensive experiments on five large-scale real-world projects demonstrate that the proposed model significantly outperforms existing techniques.