Structural contrastive learning based automatic bug triaging

Abstract

Bug triaging is crucial for software maintenance, as it matches developers with bug reports they are most qualified to handle. This task has gained importance with the growth of the open-source community. Traditionally, methods have emphasized semantic classification of bug reports, but recent approaches focus on the associations between bugs and developers. Leveraging latent patterns from bug-fixing records can enhance triaging predictions; however, the limited availability of these records presents a significant challenge. This scarcity highlights a broader issue in supervised learning: the inadequacy of labeled data and the underutilization of unlabeled data. To address these limitations, we propose a novel framework named SCL-BT (Structural Contrastive Learning-based Bug Triaging). This framework improves the utilization of labeled heterogeneous associations through edge perturbation and leverages unlabeled homogeneous associations via hypergraph sampling. These processes are integrated with a graph convolutional network backbone to enhance the prediction of associations and, consequently, bug triaging accuracy. Experimental results demonstrate that SCL-BT significantly outperforms existing models on public datasets. Specifically, on the Google Chromium dataset, SCL-BT surpasses the GRCNN method by 18.64\(\%\) in terms of the Top-9 Hit Ratio metric. The innovative approach of SCL-BT offers valuable insights for the research of automatic bug-triaging.