  Often, the first step in managing bug reports is related to triaging a bug to
the appropriate developer who is best suited to understand, localize, and fix
the target bug. Additionally, assigning a given bug to a particular part of a
software project can help to expedite the fixing process. However, despite the
importance of these activities, they are quite challenging, where days can be
spent on the manual triaging process. Past studies have attempted to leverage
the limited textual data of bug reports to train text classification models
that automate this process -- to varying degrees of success. However, the
textual representations and machine learning models used in prior work are
limited by their expressiveness, often failing to capture nuanced textual
patterns that might otherwise aid in the triaging process. Recently, large,
transformer-based, pre-trained neural text representation techniques such as
BERT have achieved greater performance in several natural language processing
tasks. However, the potential for using these techniques to improve upon prior
approaches for automated bug triaging is not well studied or understood.
  Therefore, in this paper we offer one of the first investigations that
fine-tunes transformer-based language models for the task of bug triaging on
four open source datasets, spanning a collective 53 years of development
history with over 400 developers and over 150 software project components. Our
study includes both a quantitative and qualitative analysis of effectiveness.
Our findings illustrate that DeBERTa is the most effective technique across the
triaging tasks of developer and component assignment, and the measured
performance delta is statistically significant compared to other techniques.
However, through our qualitative analysis, we also observe that each technique
possesses unique abilities best suited to certain types of bug reports.
