LAUSR

As the popularity of open source projects, the volume of incoming pull requests is too large, which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests. An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency. In this paper, an approach CTCPPre is proposed to predict the accepted pull requests in GitHub. CTCPPre mainly considers code features of modified changes, text features of pull requests’ description, contributor features of developers’ previous behaviors, and project features of development environment. The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated. Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82, AUC of 0.76 and F1-score of 0.88 on average. It is compared with the state of art accepted pull request prediction approach RFPredict. On average across 28 projects, CTCPPre outperforms RFPredict by 6.64%, 16.06% and 4.79% in terms of accuracy, AUC and F1-score, respectively. 随着开源项目的流行,项目收到的贡献请求的数量巨大,给负责接受或拒绝贡献请求的评审人 带来了沉重的负担。一种预测被接受的贡献请求的方法可以帮助评审人立刻拒绝低质量贡献请求,将 更多精力投入到高质量的贡献请求上。本文提出了一种CTCPPre 方法来预测GitHub 中被接受的贡献 请求。CTCPPre 主要考虑修代码变更的代码特征,贡献请求描述信息的文本特征,基于人员历史行为 的贡献者特征以及开发环境的项目特征。评估了CTCPPre 对包含221096 个贡献请求的28 个项目的 有效性。实验结果表明,与法RFPredict 预测方相比较,CTCPPre 具有良好的性能,平均准确度为0.82, 平均AUC 为0.76,平均F1 分数为0.88。在28 个项目中,CTCPPre 在准确性、AUC 和F1 分数方面 分别比RFPredict 方法高6.64%,16.06%和4.79%。