Efficient Bug Triage For Industrial Environments

Bug triage is an important task for software maintenance, especially in the industrial environment, where timely bug fixing is critical for customer experience. This process is usually done manually and often takes significant time. In this paper, we propose a machine-learning-based solution to address the problem efficiently. We argue that in the industrial environment, it is more suitable to assign bugs to software components (then to responsible developers) than to developers directly. Because developers can change their roles in industry, they may not oversee the same software module as before. We also demonstrate experimentally that assigning bugs to components rather than developers leads to much higher accuracy. Our solution is based on text-projection features extracted from bug descriptions. We use a Deep Neural Network to train the classification model. The proposed solution achieves state-of-the-art performance based on extensive experiments using multiple data sets. Moreover, our solution is computationally efficient and runs in near real-time.