  Bug triaging is a critical task in any software development project. It
entails triagers going over a list of open bugs, deciding whether each is
required to be addressed, and, if so, which developer should fix it. However,
the manual bug assignment in issue tracking systems (ITS) offers only a limited
solution and might easily fail when triagers must handle a large number of bug
reports. During the automated assignment, there are multiple sources of
uncertainties in the ITS, which should be addressed meticulously. In this
study, we develop a Markov decision process (MDP) model for an online bug
triage task. In addition to an optimization-based myopic technique, we provide
an ADP-based bug triage solution, called ADPTriage, which has the ability to
reflect the downstream uncertainty in the bug arrivals and developers'
timetables. Specifically, without placing any limits on the underlying
stochastic process, this technique enables real-time decision-making on bug
assignments while taking into consideration developers' expertise, bug type,
and bug fixing time. Our result shows a significant improvement over the myopic
approach in terms of assignment accuracy and fixing time. We also demonstrate
the empirical convergence of the model and conduct sensitivity analysis with
various model parameters. Accordingly, this work constitutes a significant step
forward in addressing the uncertainty in bug triage solutions
