  In this paper, we propose a recommendation approach -- TaskAllocator -- in
order to predict the assignment of incoming tasks to potential befitting roles.
The proposed approach, identifying team roles rather than individual persons,
allows project managers to perform better tasks allocation in case the
individual developers are over-utilized or moved on to different
roles/projects. We evaluated our approach on ten agile case study projects
obtained from the Taiga.io repository. In order to determine the
TaskAllocator's performance, we have conducted a benchmark study by comparing
it with contemporary machine learning models. The applicability of the
TaskAllocator was assessed through a plugin that can be integrated with JIRA
and provides recommendations about suitable roles whenever a new task is added
to the project. Lastly, the source code of the plugin and the dataset employed
have been made public.
