Affiliation:
1. University of Delaware, USA
2. Virginia Commonwealth University, USA
3. UserVoice, USA
Abstract
Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Reference85 articles.
1. smart-words.org. 2020. Retrieved from https://www.smart-words.org/abbreviations/text.html. smart-words.org. 2020. Retrieved from https://www.smart-words.org/abbreviations/text.html.
2. spaCy. 2020. Retrieved from https://spacy.io/. spaCy. 2020. Retrieved from https://spacy.io/.
3. Finding high-quality content in social media
4. Mining duplicate questions in stack overflow
5. REACT: An Approach for Capturing Rationale in Chat Messages
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