Unsupervised deep bug report summarization

Abstract

Bug report summarization is an effective way to reduce the considerable time in wading through numerous bug reports. Although some supervised and unsupervised algorithms have been proposed for this task, their performance is still limited, due to the particular characteristics of bug reports, including the evaluation behaviours in bug reports, the diverse sentences in software language and natural language, and the domain-specific predefined fields. In this study, we conduct the first exploration of the deep learning network on bug report summarization. Our approach, called DeepSum, is a novel stepped auto-encoder network with evaluation enhancement and predefined fields enhancement modules, which successfully integrates the bug report characteristics into a deep neural network. DeepSum is unsupervised. It significantly reduces the efforts on labeling huge training sets. Extensive experiments show that DeepSum outperforms the comparative algorithms by up to 13.2% and 9.2% in terms of F-score and Rouge-n metrics respectively over the public datasets, and achieves the state-of-the-art performance. Our work shows promising prospects for deep learning to summarize millions of bug reports.