SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub
This addresses the onboarding difficulty for new contributors in open-source software projects, though it is incremental as it builds on existing labeling approaches.
The paper tackles the problem of new contributors struggling to find suitable tasks in open-source software projects by predicting the fine-grained skills needed to solve GitHub issues, achieving an average precision of 91%, recall of 88%, and F-measure of 89% in a case study.
New contributors often struggle to find tasks that they can tackle when onboarding onto a new Open Source Software (OSS) project. One reason for this difficulty is that issue trackers lack explanations about the knowledge or skills needed to complete a given task successfully. These explanations can be complex and time-consuming to produce. Past research has partially addressed this problem by labeling issues with issue types, issue difficulty level, and issue skills. However, current approaches are limited to a small set of labels and lack in-depth details about their semantics, which may not sufficiently help contributors identify suitable issues. To surmount this limitation, this paper explores large language models (LLMs) and Random Forest (RF) to predict the multilevel skills required to solve the open issues. We introduce a novel tool, SkillScope, which retrieves current issues from Java projects hosted on GitHub and predicts the multilevel programming skills required to resolve these issues. In a case study, we demonstrate that SkillScope could predict 217 multilevel skills for tasks with 91% precision, 88% recall, and 89% F-measure on average. Practitioners can use this tool to better delegate or choose tasks to solve in OSS projects.