  Selecting an appropriate task is challenging for contributors to Open Source
Software (OSS), mainly for those who are contributing for the first time.
Therefore, researchers and OSS projects have proposed various strategies to aid
newcomers, including labeling tasks. We investigate the automatic labeling of
open issues strategy to help the contributors to pick a task to contribute. We
label the issues with API-domains--categories of APIs parsed from the source
code used to solve the issues. We plan to add social network analysis metrics
from the issues conversations as new predictors. By identifying the skills, we
claim the contributor candidates should pick a task more suitable. We analyzed
interview transcripts and the survey's open-ended questions to comprehend the
strategies used to assist in onboarding contributors and used to pick up an
issue. We applied quantitative studies to analyze the relevance of the labels
in an experiment and compare the strategies' relative importance. We also mined
issue data from OSS repositories to predict the API-domain labels with
comparable precision, recall, and F-measure with the state-of-art. We plan to
use a skill ontology to assist the matching process between contributors and
tasks. By analyzing the confidence level of the matching instances in
ontologies describing contributors' skills and tasks, we might recommend issues
for contribution. So far, the results showed that organizing the issues--which
includes assigning labels is seen as an essential strategy for diverse roles in
OSS communities. The API-domain labels are relevant for experienced
practitioners. The predictions have an average precision of 75.5%. Labeling the
issues indicates the skills involved in an issue. The labels represent possible
skills in the source code related to an issue. By investigating this research
topic, we expect to assist the new contributors in finding a task.
