基于协作贡献网络的开源项目开发者推荐  

Developer recommendation for open-source projects based on collaborative contribution network

作　　者：游兰[1,2] 张雨昂 刘源 陈智军[1,2] 王伟[4] 曾星 何张玮 YOU Lan;ZHANG Yuang;LIU Yuan;CHEN Zhijun;WANG Wei;ZENG Xing;HE Zhangwei(College of Computer Science,Hubei University,Wuhan Hubei 430062,China;Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Wuhan Hubei 430062,China;Key Laboratory of Intelligent Sensing System and Security,Ministry of Education(Hubei University),Wuhan Hubei 430062,China;School of Data Science and Engineering,East China Normal University,Shanghai 200062,China)

机构地区：[1]湖北大学计算机学院,武汉430062 [2]大数据智能分析与行业应用湖北省重点实验室(湖北大学),武汉430062 [3]智能感知系统与安全教育部重点实验室(湖北大学),武汉430062 [4]华东师范大学数据科学与工程学院,上海200062

出　　处：《计算机应用》2025年第4期1213-1222,共10页journal of Computer Applications

基　　金：湖北省重点研发计划项目(2022BAA044)。

摘　　要：面向开源项目推荐开发人员对开源生态建设具有重要意义。区别于传统软件开发,开源领域的开发者、项目、组织及相互关系体现了开放式协作项目的特点,而它们蕴含的语义有助于精准推荐开源项目的开发者。因此,提出一种基于协作贡献网络(CCN)的开发者推荐(DRCCN)方法。首先,利用开源软件(OSS)开发者、OSS项目、OSS组织之间的贡献关系构建CCN;其次,基于CCN构建一个3层深度的异构GraphSAGE(Graph SAmple and aggreGatE)图神经网络(GNN)模型,预测开发者节点和开源项目节点之间的链接,从而产生相应的嵌入对;最后,根据预测结果,采用K最近邻(KNN)算法完成开发者推荐。在GitHub数据集上训练和测试模型的实验结果表明,相较于序列推荐的对比学习模型CL4SRec(Contrastive Learning for Sequential Recommendation),DRCCN在精确率、召回率和F1值这3个指标上分别提升了约10.7%、2.6%和4.2%。因此,所提模型可以为开源社区项目的开发者推荐提供重要的参考依据。Recommending developers for open-source projects is of great significance to the construction of open-source ecology.Different from traditional software development,developers,projects,organizations and correlations in the opensource field reflect the characteristics of open collaborative projects,and their embedded semantics help to recommend developers accurately for open-source projects.Therefore,a Developer Recommendation method based on Collaborative Contribution Network(DRCCN)was proposed.Firstly,a CCN was constructed by utilizing the contribution relationships among Open-Source Software(OSS)developers,OSS projects and OSS organizations.Then,based on CCN,a three-layer deep heterogeneous GraphSAGE(Graph SAmple and aggreGatE)Graph Neural Network(GNN)model was constructed to predict the links between developer nodes and open-source project nodes,so as to generate the corresponding embedding pairs.Finally,according to the prediction results,the K-Nearest Neighbor(KNN)algorithm was adopted to complete the developer recommendation.The proposed model was trained and tested on GitHub dataset,and the experimental results show that compared to the contrastive learning model for sequential recommendation CL4SRec(Contrastive Learning for Sequential Recommendation),DRCCN improves the precision,recall,and F1 score by approximately 10.7%,2.6%,and 4.2%,respectively.It can be seen that the proposed model can provide important reference for the developer recommendation of open-source community projects.

关 键 词：开源生态 开发者推荐 异构信息网络 图神经网络 开源软件 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]