  Open Source Software projects add labels to open issues to help contributors
choose tasks. However, manually labeling issues is time-consuming and
error-prone. Current automatic approaches for creating labels are mostly
limited to classifying issues as a bug/non-bug. In this paper, we investigate
the feasibility and relevance of labeling issues with the domain of the APIs
required to complete the tasks. We leverage the issues' description and the
project history to build prediction models, which resulted in precision up to
82% and recall up to 97.8%. We also ran a user study (n=74) to assess these
labels' relevancy to potential contributors. The results show that the labels
were useful to participants in choosing tasks, and the API-domain labels were
selected more often than the existing architecture-based labels. Our results
can inspire the creation of tools to automatically label issues, helping
developers to find tasks that better match their skills.
