ISPY：从社区实时聊天中自动提取问题-解决方案对
ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats
摘要：
协作实时聊天作为一种开发沟通工具越来越受欢迎。在社区实时聊天中，开发人员可能会发布他们遇到的问题（例如，安装问题和编译问题），其他开发人员会提供可能的解决方案。因此，社区实时聊天包含了报告问题及其相应解决方案的丰富信息集，如果及时提取和恢复，这些信息对知识共享和未来重用非常有用。然而，由于实时聊天数据中交错对话的噪声性质，准确地挖掘这些知识仍然具有挑战性。在本文中，我们首先制定了从开发人员实时聊天数据中提取问题解决方案对的问题，并提出了一种自动化方法，名为ISPY，基于自然语言处理和深度学习技术与定制的增强功能，以解决这个问题。具体来说，ISPY自动化了三个任务：1）整理实时聊天记录，采用前馈神经网络将会话历史自动整理成单独的对话; 2）使用新型卷积神经网络（CNN）检测讨论问题的对话，该网络由基于BERT的话语嵌入层，上下文感知对话嵌入层和输出层组成; 3）基于相同的CNN结构但具有不同的馈送输入，提取适当的话语并将它们组合为相应的解决方案。为了评估ISPY，我们将其与六个基线进行比较，利用包含750个对话框的数据集，包括171个问题解决方案对，并评估来自八个开源社区的ISPY。结果表明，对于问题检测，我们的方法实现了76%的F1，并且比所有基线高出30%。我们的方法实现了63%的F1的溶液提取和超过基线的20%。
英文摘要：
Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
