  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. As manual bug assignment is a labor-intensive task,
especially for large-scale software projects, many machine-learning-based
approaches have been proposed to automatically triage bug reports. 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 local topological structures in a graph with
two sampling strategies by considering both node importance and edge
importance. 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 dynamic DCNs. We evaluated our approach's
effectiveness by comparing it with several state-of-the-art graph
representation learning methods in two domain-specific tasks that belong to
node classification. In the two 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.
