Can I Solve It? Identifying APIs Required to Complete OSS Task

TL;DR

This paper explores automatic labeling of open source issues with relevant API domains using description and history data, achieving high accuracy and aiding contributors in task selection.

Contribution

It introduces a novel approach to label issues with API domain information, improving upon traditional bug/non-bug classification methods.

Findings

Precision up to 82% in API domain prediction

Recall up to 97.8% in API domain prediction

API labels are more useful for task selection than existing labels

Abstract

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.