What Drives Issue Resolution Speed? An Empirical Study of Scientific Workflow Systems on GitHub

TL;DR

This empirical study analyzes 21,116 issues in GitHub-hosted Scientific Workflow Systems to identify factors influencing issue resolution speed, revealing that project management practices like labeling and assigning issues can significantly speed up resolution times.

Contribution

It provides the first comprehensive analysis of issue resolution dynamics in SWSs, highlighting key project and contributor factors affecting resolution speed.

Findings

68.91% of issues are closed

Half of issues are resolved within 18.09 days

Labeling and assigning issues are linked to faster resolution

Abstract

Scientific Workflow Systems (SWSs) play a vital role in enabling reproducible, scalable, and automated scientific analysis. Like other open-source software, these systems depend on active maintenance and community engagement to remain reliable and sustainable. However, despite the importance of timely issue resolution for software quality and community trust, little is known about what drives issue resolution speed within SWSs. This paper presents an empirical study of issue management and resolution across a collection of GitHub-hosted SWS projects. We analyze 21,116 issues to investigate how project characteristics, issue metadata, and contributor interactions affect time-to-close. Specifically, we address two research questions: (1) how issues are managed and addressed in SWSs, and (2) how issue and contributor features relate to issue resolution speed. We find that 68.91% of issues are closed, with half of them resolved within 18.09 days. Our results show that although SWS projects follow structured issue management practices, the issue resolution speed varies considerably across systems. Factors such as labeling and assigning issues are associated with faster issue resolution. Based on our findings, we make recommendations for developers to better manage SWS repository issues and improve their quality.