# Automatic Bug Triaging via Deep Reinforcement Learning
## 1 Introduction
In the DMSF model, we adopted a recurrent neural network (RNN) to extract the multidimensional features of bug reports and capture the diversity of text structure, and the multiplication between elements of the feature vector was obtained for feature fusion to obtain high-quality information. In the ODM model, the feature information of bug reports was \"state\", and the bug report process was assigned to one of the developers with a probability of \"action\" in reinforcement learning (RL). By analyzing the multidimensional features, the probabilities of the developers to be selected for fixing the bugs can be obtained; hence, we can identify the developer with the highest probability value. Subsequently, based on the current specific feedback results and their evaluation, the corresponding rewards can be presented. Therefore, the model can perform self-training and adapt to the association between bug reports and developers to achieve the expected results. We evaluated the performance of BT-RL experimentally based on four open bug repositories (NetBeans, OpenOffice, Mozilla, and Eclipse). The experimental results show that BT-RL is superior to the baseline. From the perspective of model allocation accuracy, BT-RL achieved 52%, 54%, 68%, and 78% in terms of the Top-5 on the OpenOffice, NetBeans, Mozilla, and Eclipse datasets, respectively, and improved the LDA_SVM by up to 4%, 11%, 9%, and 8%, respectively. Our model significantly improves the models proposed by researchers in recent years. After we train the model, it can be deployed on the server to work in real time. Figure 1 shows the procedure of the real-time bug triaging. Specifically, a developer identifies a bug, then he/she will submit a bug report with necessary information describing this bug to the bug management system. Memory replay will store the information of the bug reports and the developers. Then, the BT-RL model will leverage the information stored in memory replay to enhance its performance, and assign this bug report to the suitable developer. To summarize, the main contributions of this study are as follows:  
1. A bug triaging model based on RL is presented, in which feature extraction is performed using neural networks, and newly created bug reports can be assigned online in real time. 2. A bidirectional RNN that integrates multidimensional features is adopted, and it can capture the information of different levels more comprehensively while rendering the representation of bug reports more robust for bug triaging, compared with the SVM and the method proposed by [PERSON] [20]. 3. The BT-RL model on real-world datasets (NetBeans, OpenOffice, Mozilla, and Eclipse) is validated. Comprehensive experimental results show that the BT-RL model performs better than state-of-the-art models. The remainder of this paper is structured as follows. Section 2 lists the related works. Section 3 introduces our motivation. We present the details of our approach in Section 4. We evaluate our approach and analyze its performance in Section 5. In Section 6, we discuss threats and validity in our experiments to our work. Finally, in Section 7, we conclude this work and discuss future work.