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arXiv 2601.03430cs.SEcs.CY
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对OSS4SG与传统OSS社区和编码模式的实证分析

An Empirical Analysis of Community and Coding Patterns in OSS4SG vs. Conventional OSS

Queen’s University(女王大学)

Mohamed Ouf, Shayan Noei, Zeph Van Iterson, Mariam Guizani, Ying Zou

AI总结:

本文通过对比OSS4SG与传统OSS项目,发现前者社区更稳定且参与度更一致,而后者更吸引贡献者但存在高流失率。

摘要:

开源软件用于社会公益(OSS4SG)项目旨在解决关键的社会挑战,如医疗保健可及性和社区安全。理解这些项目中的社区动态和贡献者模式对于确保其可持续性和长期影响至关重要。然而,尽管大量研究集中在传统开源软件(OSS)上,但很少有人了解OSS4SG的使命驱动性质如何影响其开发实践。为此,我们对1,039个GitHub仓库进行了大规模实证研究,其中包括422个OSS4SG项目和617个传统OSS项目,以比较社区结构、贡献者参与度和编码实践。我们的发现表明,OSS4SG项目形成了显著更稳定和'粘性'(63.4%)的社区,而传统OSS项目则更'磁性'(75.4%),吸引大量贡献者流失。OSS4SG项目全年表现出一致的参与度,而传统OSS社区则表现出季节性波动。此外,OSS4SG项目严重依赖核心贡献者进行代码质量和问题解决,而传统OSS项目则利用非正式贡献者进行问题解决,核心贡献者主要专注于代码质量。

英文摘要:

Open Source Software for Social Good (OSS4SG) projects aim to address critical societal challenges, such as healthcare access and community safety. Understanding the community dynamics and contributor patterns in these projects is essential for ensuring their sustainability and long-term impact. However, while extensive research has focused on conventional Open Source Software (OSS), little is known about how the mission-driven nature of OSS4SG influences its development practices. To address this gap, we conduct a large-scale empirical study of 1,039 GitHub repositories, comprising 422 OSS4SG and 617 conventional OSS projects, to compare community structure, contributor engagement, and coding practices. Our findings reveal that OSS4SG projects foster significantly more stable and "sticky" (63.4%) communities, whereas conventional OSS projects are more "magnetic" (75.4%), attracting a high turnover of contributors. OSS4SG projects also demonstrate consistent engagement throughout the year, while conventional OSS communities exhibit seasonal fluctuations. Additionally, OSS4SG projects rely heavily on core contributors for both code quality and issue resolution, while conventional OSS projects leverage casual contributors for issue resolution, with core contributors focusing primarily on code quality.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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