  Users use Issue Tracking Systems to keep track and manage issue reports in
their repositories. An issue is a rich source of software information that
contains different reports including a 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. Thus, automatic
approaches are proposed to help facilitate the management of issue reports.
  This paper describes CatIss, an automatic CATegorizer of ISSue reports which
is built upon the Transformer-based pre-trained RoBERTa model. CatIss
classifies issue reports into three main categories of Bug reports,
Enhancement/feature requests, and Questions. First, the datasets provided for
the NLBSE tool competition are cleaned and preprocessed. Then, the pre-trained
RoBERTa model is fine-tuned on the preprocessed dataset. Evaluating CatIss on
about 80 thousand issue reports from GitHub, indicates that it performs very
well surpassing the competition baseline, TicketTagger, and achieving 87.2%
F1-score (micro average). Additionally, as CatIss is trained on a wide set of
repositories, it is a generic prediction model, hence applicable for any unseen
software project or projects with little historical data. Scripts for cleaning
the datasets, training CatIss, and evaluating the model are publicly available.
