From May 2019, GitHub launched sponsor mechanism indicating that GitHub is moving towards deeper integration of open source development and economic support. It will bring more comprehensive and diversified support to the open source community. However, the number of developers profiting from the sponsor mechanism follows a long tail distribution. Our study found that only 31% of developers who started the sponsor mechanism received rewards, and 39.3% of them only received a reward of one dollar.

Our work focuses on identifying what factors affect the availability of sponsorship for developers in open source community. We start by defining 45 features to characterize the developers in four dimensions i.e. Personality, Advertisement, Repository and Behavior. The results of statistical analysis indicate that most of the proposed features differ significantly between the ones who received rewards (short for MTs_Yes) from those that are not. After that, we build machine learning model based on the proposed features to predict MTs_Yes. Compared with the existing work, results show that our method outperforms baselines by 30% for AUC (Area Under the Curve). In addition, we investigated the relative contribution of features in detecting MTs_Yes and analyzed the important features by using an interpretable model SHAP. Finally, based on the experimental results, we put forward corresponding and practical suggestions for developers who want to receive rewards so as to make the community of open source projects develop more harmonious.

2019年5月起，GitHub推出赞助商机制，标志着GitHub正朝着开源开发与经济支持的更深层次融合迈进。将为开源社区带来更加全面、多元化的支持。然而，从赞助商机制中获利的开发者数量遵循长尾分布。我们的研究发现，启动赞助机制的开发者中只有31%获得了奖励，其中39.3%的人只获得了1美元的奖励。

我们的工作重点是确定哪些因素会影响开源社区开发者获得赞助的机会。我们首先定义 45 个特征，从四个维度来描述开发人员的特征，即个性、广告、存储库和行为。统计分析的结果表明，大多数提出的特征在获得奖励（MTs_Yes 的缩写）的人与没有获得奖励的人之间存在显着差异。之后，我们根据所提出的特征构建机器学习模型来预测MTs_Yes。与现有工作相比，结果表明我们的方法在AUC（曲线下面积）方面优于基线 30%。此外，我们研究了特征在检测MTs_Yes中的相对贡献，并使用可解释模型SHAP分析了重要特征。最后，根据实验结果，我们为想要获得奖励的开发者提出了相应的、实用的建议，使开源项目的社区发展更加和谐。