Deep Learning Based Valid Bug Reports Determination and Explanation

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.