Exploratory Study of Slack Q&A Chats as a Mining Source for Software Engineering Tools

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR) Pub Date : 2019-05-26 DOI:10.1109/MSR.2019.00075

Preetha Chatterjee, Kostadin Damevski, L. Pollock, Vinay Augustine, Nicholas A. Kraft

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引用次数: 50

Abstract

Modern software development communities are increasingly social. Popular chat platforms such as Slack host public chat communities that focus on specific development topics such as Python or Ruby-on-Rails. Conversations in these public chats often follow a Q&A format, with someone seeking information and others providing answers in chat form. In this paper, we describe an exploratory study into the potential use-fulness and challenges of mining developer Q&A conversations for supporting software maintenance and evolution tools. We designed the study to investigate the availability of information that has been successfully mined from other developer communications, particularly Stack Overflow. We also analyze characteristics of chat conversations that might inhibit accurate automated analysis. Our results indicate the prevalence of useful information, including API mentions and code snippets with descriptions, and several hurdles that need to be overcome to automate mining that information.

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Slack问答聊天作为软件工程工具挖掘源的探索性研究

现代软件开发社区越来越社会化。流行的聊天平台，如Slack，提供公共聊天社区，专注于特定的开发主题，如Python或Ruby-on-Rails。这些公共聊天中的对话通常采用问答形式，有人寻求信息，其他人以聊天形式提供答案。在本文中，我们描述了一项探索性研究，该研究探讨了挖掘开发人员问答对话的潜在有用性和挑战，以支持软件维护和进化工具。我们设计这项研究是为了调查从其他开发人员通信中成功挖掘的信息的可用性，特别是Stack Overflow。我们还分析聊天对话的特征，这些特征可能会抑制准确的自动化分析。我们的结果表明了有用信息的普遍性，包括API提及和带有描述的代码片段，以及自动化挖掘这些信息需要克服的几个障碍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

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