Software projects usually receive a large number of submitted bug reports every day. Manually triaging the bug reports is often time‐consuming and error‐prone; thus, it is necessary to automatically assign the bug reports to the suitable developers for bug repair, with the help of bug tracking systems. Aiming to reducing the time consumption and mismatch of bug report assignments, we present a developer recommendation model for bug repair based on weighted recurrent neural network, namely, DTPM, which contains two parts: One obtains multisource semantic information of bug reports and fuses them into high‐dimensional semantic feature vectors, and the other combines a penalty matrix into a single hidden layer neural network to obtain more reasonable developer recommendations. We conduct experiments on five datasets of open bug repositories (NetBeans, OpenOffice, GCC, Mozilla, and Eclipse), and the experimental results show that DTPM can achieve better performance than state‐of‐the‐art models LDA_KL, LDA_KL, LDA_SVM, DERTOM, DREX, and DeepTriage.

中文翻译：

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通过与错误修复过程的交互来推断开发活动，从而获得更准确的开发人员推荐

软件项目通常每天都会收到大量已提交的错误报告。手动分类错误报告通常既费时又容易出错。因此，有必要在错误跟踪系统的帮助下，将错误报告自动分配给合适的开发人员以进行错误修复。为了减少错误报告分配的时间消耗和不匹配，我们提出了一种基于加权递归神经网络的错误修复开发人员推荐模型DTPM，该模型包含两个部分：一个获取错误报告的多源语义信息并将其融合到其中高维语义特征向量，另一个将惩罚矩阵组合到单个隐藏层神经网络中，以获得更合理的开发人员建议。我们对五个开放式错误存储库（NetBeans，