How the R community creates and curates knowledge: an extended study of stack overflow and mailing lists

Alexey Zagalsky*, Daniel M. German, Margaret Anne Storey, Carlos Gómez Teshima, Germán Poo-Caamaño

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

One of the effects of social media’s prevalence in software development is the many flourishing communities of practice where users share a common interest. These large communities use many different communication channels, but little is known about how they create, share, and curate knowledge using such channels. In this paper, we report a mixed methods study of how one community of practice, the R software development community, creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R tag in Stack Overflow and the R-Help mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple users explicitly collaborate to build knowledge, and crowdsourced, where individuals primarily work independently of each other. Moreover, we take a unique approach at slicing the data based on question score and participation activities over time. Our study reveals participation patterns, showing the existence of prolific contributors: users who are active across both channels and are responsible for a large proportion of the answers, serving as a bridge of knowledge. The key contributions of this paper are: a characterization of knowledge artifacts that are exchanged by this community of practice; the reasons why users choose one channel over the other; and insights on the community participation patterns, which indicate an evolution of the community and a shift from knowledge creation to knowledge curation.

Original languageEnglish
Pages (from-to)953-986
Number of pages34
JournalEmpirical Software Engineering
Volume23
Issue number2
DOIs
StatePublished - 1 Apr 2018
Externally publishedYes

Keywords

  • Empirical study
  • Mailing list
  • Mining software repositories
  • Qualitative study
  • R
  • Stack overflow
  • Survey

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