  Successful open source communities are constantly looking for new members and
helping them become active developers. A common approach for developer
onboarding in open source projects is to let newcomers focus on relevant yet
easy-to-solve issues to familiarize themselves with the code and the community.
The goal of this research is twofold. First, we aim at automatically
identifying issues that newcomers can resolve by analyzing the history of
resolved issues by simply using the title and description of issues. Second, we
aim at automatically identifying issues, that can be resolved by newcomers who
later become active developers. We mined the issue trackers of three large open
source projects and extracted natural language features from the title and
description of resolved issues. In a series of experiments, we optimized and
compared the accuracy of four supervised classifiers to address our research
goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the
first goal while for the second goal, Decision Tree achieved a precision of 92%
(F1-score 91%). A qualitative evaluation gave insights on what information in
the issue description is helpful for newcomers. Our approach can be used to
automatically identify, label, and recommend issues for newcomers in open
source software projects based only on the text of the issues.
