A spatial–temporal graph neural network framework for automated software bug triaging

Abstract

The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development. Since manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine learning-based approaches have been proposed to triage bug reports automatically. Although developer collaboration networks (DCNs) are dynamic and evolving in the real world, most automated bug triaging approaches focus on static tossing graphs at a single time slice. Also, none of the previous studies consider periodic interactions among developers. To address the problems mentioned above, in this article, we propose a novel spatial–temporal dynamic graph neural network (ST-DGNN) framework, including a joint random walk (JRWalk) mechanism and a graph recurrent convolutional neural network (GRCNN) model. In particular, JRWalk aims to sample topological structures in a developer collaboration network with two sampling strategies by considering both developer reputation and interaction preference. GRCNN has three components with the same structure, i.e., hourly-periodic, daily-periodic, and weekly-periodic components, to learn the spatial–temporal features of nodes on dynamic DCNs. We evaluated our approach’s effectiveness by comparing it with several state-of-the-art graph representation learning methods in three domain-specific tasks (i.e., the bug fixer prediction task and two downstream tasks of graph representation learning: node classification and link prediction). In the three tasks, experiments on two real-world, large-scale developer collaboration networks collected from the Eclipse and Mozilla projects indicate that the proposed approach outperforms all the baseline methods on three different time scales (i.e., long-term, medium-term, and short-term predictions) in terms of F1−score.