Abstract: Software defect repair (also known as software bug fixing) is a necessary part of software quality assurance. In the collective-intelligence-based software development environment on the Internet, improving the efficiency and effectiveness of software bug triaging can help raise bug fixing rates and reduce maintenance costs. Nowadays, automatic bug triaging approaches based on machine learning have become mainstream, but they also have some specific problems, such as hand-crafted features and an insufficient ability to represent texts. Considering successful applications of deep learning in the field of natural language processing, researchers have recently tried to introduce deep learning into the field of automatic bug triaging, to improve the performance of predicting the right bug fixer significantly. However, different types of neural networks have their limitations. To address the problems mentioned above, in this study, we regard bug triaging as a text classification problem and propose an automatic bug triaging approach based on hybrid neural networks and an attention mechanism, called Atten-CRNN. Because Atten-CRNN combines the advantages of a convolutional neural network, a recurrent neural network, and an attention mechanism, it can capture essential text features and sequence features of bug reports more effectively and then provide more accurate fixer recommendation services for software development and maintenance. An empirical study was conducted on two popular large-scale open-source software projects, namely Eclipse and Mozilla. The experimental results obtained from over 200 000 bug reports indicate that Atten-CRNN achieves higher prediction accuracy than convolutional neural networks and recurrent neural networks, regardless of the attention mechanism.