BugSum: Deep Context Understanding for Bug Report Summarization

During collaborative software development, bug reports are dynamically maintained and evolved as a part of a software project. For a historical bug report with complicated discussions, an accurate and concise summary can enable stakeholders to reduce the time effort perusing the entire content. Existing studies on bug report summarization, based on whether supervised or unsupervised techniques, are limited due to their lack of consideration of the redundant information and disapproved standpoints among developers' comments. Accordingly, in this paper, we propose a novel unsupervised approach based on deep learning network, called BugSum. Our approach integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions. In addition, a dynamic selection strategy is employed to optimize the comprehensiveness of the auto-generated summary represented by limited words. Extensive experiments show that our approach outperforms 8 comparative approaches over two public datasets. In particular, the probability of adding controversial sentences that are clearly disapproved by other developers during the discussion, into the summary is reduced by up to 69.6%.