Bug triaging approach based on recurrent neural networks. With the increasing size of open source software projects, assigning suitable developers for bug reports (i.e., bug triaging) is becoming more and more difficult. Moreover, the efficiency of bug repairing will likely be reduced if the bugs are assigned to inappropriate developers. Therefore, it is necessary to provide an automatic bug triaging technique for the project managers to better assign bug reports. Existing work for this task mainly focuses on analyzing the text and metadata in bug reports to characterize the relationships between developers and bug reports, while the active level of developers is largely ignored. A shortcoming of these methods is that they may lead to poor performance when developers with different active levels have similar characteristics. This paper proposes a learning model named DeepTriage based on the recurrent neural networks. On the one hand, the ordered natural language text in bug reports is mapped into high-level features by a bidirectional RNN. On the other hand, developer’s active level is extracted and transformed into high-level features through a single directional RNN. Then, the features of text and developer’s active level are combined and learned from bug reports with known fixers. Experimental results on four different open-source data sets (e.g., Eclipse) show that DeepTriage has significantly improved the accuracy of bug triaging compared with existing work.