  The issue tracking system (ITS) is a rich data source for data-driven
decision-making. Different characteristics of bugs, such as severity, priority,
and time to fix, provide a clear picture of an ITS. Nevertheless, such
information may be misleading. For example, the exact time and the effort spent
on a bug might be significantly different from the actual reporting time and
the fixing time. Similarly, these values may be subjective, e.g., severity and
priority values are assigned based on the intuition of a user or a developer
rather than a structured and well-defined procedure. Hence, we explore the
evolution of the bug dependency graph together with priority and severity
levels to explore the actual triage process. Inspired by the idea of the
"Wayback Machine" for the World Wide Web, we aim to reconstruct the historical
decisions made in the ITS. Therefore, any bug prioritization or bug triage
algorithms/scenarios can be applied in the same environment using our proposed
ITS Wayback Machine. More importantly, we track the evolutionary metrics in the
ITS when a custom triage/prioritization strategy is employed. We test the
efficiency of the proposed algorithm using data extracted from three
open-source projects. Our empirical study sheds light on the overlooked
evolutionary metrics--e.g., overdue bugs and developers' loads--which are
facilitated via our proposed past-event re-generator.
