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Collaborative live chats are gaining popularity as a developmentcommunication tool. In community live chatting, developers are likely to postissues they encountered (e.g., setup issues and compile issues), and otherdevelopers respond with possible solutions. Therefore, community live chatscontain rich sets of information for reported issues and their correspondingsolutions, which can be quite useful for knowledge sharing and future reuse ifextracted and restored in time. However, it remains challenging to accuratelymine such knowledge due to the noisy nature of interleaved dialogs in live chatdata. In this paper, we first formulate the problem of issue-solution pairextraction from developer live chat data, and propose an automated approach,named ISPY, based on natural language processing and deep learning techniqueswith customized enhancements, to address the problem. Specifically, ISPYautomates three tasks: 1) Disentangle live chat logs, employing a feedforwardneural network to disentangle a conversation history into separate dialogsautomatically; 2) Detect dialogs discussing issues, using a novel convolutionalneural network (CNN), which consists of a BERT-based utterance embedding layer,a context-aware dialog embedding layer, and an output layer; 3) Extractappropriate utterances and combine them as corresponding solutions, based onthe same CNN structure but with different feeding inputs. To evaluate ISPY, wecompare it with six baselines, utilizing a dataset with 750 dialogs including171 issue-solution pairs and evaluate ISPY from eight open source communities.The results show that, for issue-detection, our approach achieves the F1 of76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63%for solution-extraction and outperforms the baselines by 20%.

ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats