Software-related Slack Chats with Disentangled Conversations
Software-related Slack Chats with Disentangled Conversations
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DOI：
10.1145/3379597.3387493
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Preetha Chatterjee;Kostadin Damevski;Nicholas A. Kraft;L. Pollock
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文献类型：
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作者：
Preetha Chatterjee;Kostadin Damevski;Nicholas A. Kraft;L. Pollock
开发人员比以往任何时候都更多地参与公共聊天社区来询问和回答软件开发问题。 Slack 拥有超过一千万的日活跃用户，是最受欢迎的聊天平台之一，拥有许多专注于软件开发技术（例如 python、react）的活跃频道。先前的研究表明，公开的 Slack 聊天记录包含有价值的信息，可以为改进自动软件维护工具提供支持，或帮助研究人员了解开发人员的困境或担忧。在本文中，我们提出了一个与软件相关的问答聊天对话数据集，该数据集由三个开放 Slack 社区（python、clojure、elm）精心策划了两年。我们的数据集包含 12,171 位用户贡献的 38,955 个对话、437,893 条话语。我们还分享了基于机器学习的定制算法的代码，该算法自动从下载的聊天记录中提取（或解开）对话。
More than ever, developers are participating in public chat communities to ask and answer software development questions. With over ten million daily active users, Slack is one of the most popular chat platforms, hosting many active channels focused on software development technologies, e.g., python, react. Prior studies have shown that public Slack chat transcripts contain valuable information, which could provide support for improving automatic software maintenance tools or help researchers understand developer struggles or concerns. In this paper, we present a dataset of software-related Q&A chat conversations, curated for two years from three open Slack communities (python, clojure, elm). Our dataset consists of 38,955 conversations, 437,893 utterances, contributed by 12,171 users. We also share the code for a customized machine-learning based algorithm that automatically extracts (or disentangles) conversations from the downloaded chat transcripts.