Free/libre open source software: What we know and what we do not know

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Citation: Kevin Crowston, Kangning Wei, James Howison, Andrea Wiggins (2012) Free/libre open source software: What we know and what we do not know. ACM Computing Surveys (RSS)
DOI (original publisher): 10.1145/2089125.2089127
Semantic Scholar (metadata): 10.1145/2089125.2089127
Sci-Hub (fulltext): 10.1145/2089125.2089127
Internet Archive Scholar (fulltext): Free/libre open source software: What we know and what we do not know
Download: http://floss.syr.edu/system/files/CrowstonFLOSSReviewPaperPreprint.pdf
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Summary (Abstract)

Review of published empirical literature where FLOSS development and use are the main themes: 138 papers from 30 journals and 41 conferences, collected primarily in 2006.

Papers analyzed FLOSS at individual, group, organization, and societal levels, with group analysis comprising 57% of collection.

Case study was the most common form of analysis for all levels, 42% of total. Survey second most common at 24% of total. Case study papers were weak in identifying data collection methods.

52% of studies based on secondary data. Only 35% of sampled papers included references to theory.

Coding of the sample indicated use of the inputs-mediators-outputs-inputs model, drawing on decades of small group research, unsurprising as much FLOSS development occurs in small groups.

Inputs

  • Member characteristics
  • Project characteristics
  • Technology use

Processes

  • Development practices
  • Social processes

Outputs

  • Team performance
  • Software implementation
  • Software/project evolution

Studies measuring success mostly used some form of code quality.

All areas need more research.

Methodological challenges include use of incomplete archival data, self-reported data, sampling strategies focused on established projects, whether levels of data, analysis and theory match up, and paucity of longitudinal studies.