BugListener is evaluated on six open source projects. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network 2) Identify the bug-report dialogs from separated dialogs by modeling the original dialog to the graph-structured dialog and leveraging the graph neural network to learn the contextual information 3) Synthesize the bug reports by utilizing the TextCNN model and Transfer Learning network to classify the sentences into three groups: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks.
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