ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats
Lin Shi, Ziyou Jiang, Ye Yang, Xiao Chen, Yumin Zhang, Fangwen Mu,, Hanzhi Jiang, Qing Wang

TL;DR
This paper introduces ISPY, an automated deep learning-based system for extracting issue-solution pairs from noisy developer live chat logs, enhancing knowledge sharing and reuse in software communities.
Contribution
ISPY is the first approach to automatically disentangle, detect issue discussions, and extract solutions from live chat data using customized neural network models.
Findings
Issue detection F1 score of 76%, outperforming baselines by 30%.
Solution extraction F1 score of 63%, outperforming baselines by 20%.
Effective automatic extraction of issue-solution pairs from noisy chat logs.
Abstract
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.…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Wikis in Education and Collaboration
