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.

翻译：科学工作流系统在实现可重复、可扩展和自动化的科学分析中发挥着至关重要的作用。与其他开源软件类似，这些系统依赖积极的维护和社区参与以保持可靠性和可持续性。然而，尽管及时解决问题对软件质量和社区信任至关重要，但人们对驱动科学工作流系统内问题解决速度的因素知之甚少。本文通过对GitHub托管的一系列科学工作流项目进行实证研究，探讨问题管理与解决机制。我们分析了21,116个问题，研究项目特征、问题元数据及贡献者互动如何影响问题关闭时间。具体而言，我们关注两个研究问题：（1）科学工作流系统中问题如何被管理和处理；（2）问题特征与贡献者特征如何与问题解决速度相关联。研究发现68.91%的问题被关闭，其中半数在18.09天内得到解决。结果表明，虽然科学工作流项目遵循结构化的问题管理实践，但不同系统间的问题解决速度差异显著。标签标记和问题分配等因素与更快的问题解决速度相关。基于研究结果，我们为开发者提出改进科学工作流仓库问题管理及提升系统质量的建议。