  Proper documentation plays an important role in successful software
management and maintenance. Software repositories such as GitHub host a large
number of software entities. Developers collaboratively discuss, implement,
use, and share these entities. 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 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. In this work, 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. We train and
evaluate our models in both project-based and cross-project settings. The
latter approach provides a generic prediction model applicable to 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
83% and 75% accuracy, respectively.
