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Multiple approaches have been proposed to automatically recommend potentialdevelopers who can address bug reports. These approaches are typically designedto work for any bug report submitted to any software project. However, weconjecture that these approaches may not work equally well for all the reportsin a project. We conducted an empirical study to validate this conjecture,using three bug assignment approaches applied on 2,249 bug reports from twoopen source systems. We found empirical evidence that validates our conjecture,which led us to explore the idea of identifying and applying thebest-performing approach for each bug report to obtain more accurate developerrecommendations. We conducted an additional study to assess the feasibility ofthis idea using machine learning. While we found a wide margin of accuracyimprovement for this approach, it is far from achieving the maximum possibleimprovement and performs comparably to baseline approaches. We discusspotential reasons for these results and conjecture that the assignmentapproaches may not capture important information about the bug assignmentprocess that developers perform in practice. The results warrant futureresearch in understanding how developers assign bug reports and improvingautomated bug report assignment

Recommending Bug Assignment Approaches for Individual Bug Reports: An Empirical Investigation