  Issue tracking systems are used in the software industry for the facilitation
of maintenance activities that keep the software robust and up to date with
ever-changing industry requirements. Usually, users report issues that can be
categorized into different labels such as bug reports, enhancement requests,
and questions related to the software. Most of the issue tracking systems make
the labelling of these issue reports optional for the issue submitter, which
leads to a large number of unlabeled issue reports. In this paper, we present a
state-of-the-art method to classify the issue reports into their respective
categories i.e. bug, enhancement, and question. This is a challenging task
because of the common use of informal language in the issue reports. Existing
studies use traditional natural language processing approaches adopting
key-word based features, which fail to incorporate the contextual relationship
between words and therefore result in a high rate of false positives and false
negatives. Moreover, previous works utilize a uni-label approach to classify
the issue reports however, in reality, an issue-submitter can tag one issue
report with more than one label at a time. This paper presents our approach to
classify the issue reports in a multi-label setting. We use an off-the-shelf
neural network called RoBERTa and fine-tune it to classify the issue reports.
We validate our approach on issue reports belonging to numerous industrial
projects from GitHub. We were able to achieve promising F-1 scores of 81%, 74%,
and 80% for bug reports, enhancements, and questions, respectively. We also
develop an industry tool called Automatic Issue Classifier (AIC), which
automatically assigns labels to newly reported issues on GitHub repositories
with high accuracy.
