  Multiple approaches have been proposed to automatically recommend potential
developers who can address bug reports. These approaches are typically designed
to work for any bug report submitted to any software project. However, we
conjecture that these approaches may not work equally well for all the reports
in a project. We conducted an empirical study to validate this conjecture,
using three bug assignment approaches applied on 2,249 bug reports from two
open source systems. We found empirical evidence that validates our conjecture,
which led us to explore the idea of identifying and applying the
best-performing approach for each bug report to obtain more accurate developer
recommendations. We conducted an additional study to assess the feasibility of
this idea using machine learning. While we found a wide margin of accuracy
improvement for this approach, it is far from achieving the maximum possible
improvement and performs comparably to baseline approaches. We discuss
potential reasons for these results and conjecture that the assignment
approaches may not capture important information about the bug assignment
process that developers perform in practice. The results warrant future
research in understanding how developers assign bug reports and improving
automated bug report assignment
