Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems

TL;DR

This paper explores automatic API-domain labeling of open source issues to help newcomers identify suitable tasks, using social network analysis and machine learning to improve onboarding and skill matching.

Contribution

It introduces an approach combining API-domain labeling and social network metrics to enhance task recommendation for OSS newcomers, with comparable prediction accuracy to state-of-the-art methods.

Findings

API-domain labels are relevant for experienced practitioners

Predictions achieve an average precision of 75.5%

Organizing issues with labels aids diverse OSS roles

Abstract

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.