AGFL: A Graph Convolutional Neural Network-Based Method for Fault Localization

Fault localization techniques have been developed for decades. Spectrum Based Fault Localization (SBFL) is a popular strategy in this research topic. However, SBFL is well known for low accuracy, mainly due to simply using a coverage matrix of program executions. In this paper, we propose a method based on graph neural network (AGFL), characterized by the adjacent matrix of the abstract syntax tree and the word vector of each program token. Referring to the Dstar, we calculate the suspiciousness of the statements and rank these statements. The experiment carried on Defects4J, a widely used benchmark, reveals that AGFL can locate 178 of the 262 studied bugs within Top-1, while state-of-the-art techniques at most locate 148 within Top-1. We also investigate the impacts of hyper-parameters (e.g., epoch and learning rate). The results show that AGFL has the best effect when the epoch is 100 and the learning rate is 0.0001. This value of epoch and learning rate increases by 66% compared to the worst on Top-1.