  Developers collaboratively discuss, implement, use, and share software
entities hosted on software repositories. Proper documentation plays an
important role in successful software management and maintenance. Users exploit
Issue Tracking Systems, a facility of software repositories, to keep track of
issue reports, to manage the workload and processes, and finally, to document
the highlight of their team's effort. An issue report is a rich source of
collaboratively curated software knowledge, and can contain a reported problem,
a request for new features, or merely a question about the software product. As
the number of these issues increases, it becomes harder to manage them
manually. GitHub provides labels for tagging issues, as a means of issue
management. However, about half of the issues in GitHub's top 1000 repositories
do not have any labels. We aim at automating the process of managing issue
reports for software teams. We propose a two-stage approach to predict both the
objective behind opening an issue and its priority level using feature
engineering methods and state-of-the-art text classifiers. To the best of our
knowledge, we are the first to fine-tune a Transformer for issue
classification. We train and evaluate our models in both project-based and
cross-project settings. The latter approach provides a generic prediction model
applicable for any unseen software project or projects with little historical
data. Our proposed approach can successfully predict the objective and priority
level of issue reports with 82% and 75% accuracy, respectively. Moreover, we
conducted human labeling and evaluation on unlabeled issues from six unseen
GitHub projects to assess the performance of the cross-project model on new
data. The model achieves 90% accuracy. We obtain 85% average Percent Agreement
and 71% Randolph's free-marginal Kappa translating to substantial agreement
among labelers.
