PCG: A joint framework of graph collaborative filtering for bug triaging

Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug-developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug-developer correlations, we introduce prototype clustering-based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios. A GCF backbone is proposed to creatively model bug triaging as a link prediction task on the bug-developer bipartite graph. A novel data augmentation scheme based on prototype clustering is proposed to mitigate the data sparsity of bug-fixing records. A novel semantic contrastive learning task is proposed to compensate for the semantic deficiency of the CF paradigm. image