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Operationalisation of Collaboration Sunbelt 2015

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The operationalisation of collaboration: in search of a definition and its consequences on
analysis
Collaboration has been defined in numerous ways. Researchers interested in collaboration at the
individual or organizational level need to pay special attention to the adoption of a specific definition, as
this is likely to have major implications for the research design and outcomes. With respect to
collaboration within open source software projects, this presentation has two objectives. Firstly, this
presentation will investigate a wide variety of definitions of collaboration from the existing literature.
Secondly, the presentation will look at theoretically informed selection of a definition. Throughout the
presentation, specific emphasis will be put on the implications of adoption of several definitions of
collaboration for the application of Social Network Analysis to the study of open source software,
particularly considering data collection and analysis. Open source software is developed in the open
where anyone can view the source code and anyone with the knowledge to do so can contribute to the
project. Because people from around the world work on these projects together using online tools, it is
a relevant setting for studying collaboration. An interesting aspect of open source collaboration is that
private resources from individuals and organizations are used to develop software that is released as a
public good. Social Network Analysis can be used to understand the network relationships between the
individuals who develop this software. Given the interest in collaboration from researchers from different
backgrounds and disciplines, similar research is likely to produce considerations to stimulate further
thoughts about definitions of collaboration in several domains and research settings.

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Operationalisation of Collaboration Sunbelt 2015

  1. 1. The Operationalisation of Collaboration: in Search of a Definition and Its Consequences On Analysis Dawn M. Foster, Guido Conaldi, Riccardo De Vita Sunbelt XXXV June 2015
  2. 2. The Context Pilot Study - define collaboration Part of Larger Research Project - PhD Dissertation Research Question for Overall Research Project: • How do software developers, who are paid by organizations for their work, collaborate within an open source software community? 2
  3. 3. The Challenge Open source software is a collaborative effort But, collaboration takes many forms And is defined in various ways Which definitions are most important? 3
  4. 4. Literature on problem solving in open source Unlikely organizations: survival depends on willingness to engage in decentralized problem solving. (e.g., Crowston & Scozzi, 2008; Mockus et , 2002, Conaldi et al. 2012) Collaboration in problem solving investigated using digital traces: email, code, bug reports, mostly separately, or multidimensionally. (e.g., Von Krogh, G., Spaeth, S., & Lakhani, K. R. 2003) Contributions close in time as proxies for collaboration. 4
  5. 5. The Approach Small Pilot Study • Interviewed 4 participants • Explored possible definitions of collaboration • Analysis of responses Network Analysis • Ego-centric relational event histories for each pilot participant • Collaboration as defined in pilot study 5
  6. 6. Research Setting Linux kernel community: • Open source software • Over 85% of contributors are paid • Neutral: competing companies contribute • 19M lines of code, 11K developers, 1200 organisations Pilot Research Question: • How do definitions of collaboration impact measurement and analysis within a decentralised organisational context? 6
  7. 7. Data Mailing list collaboration (discussion, patches, bugs) • 4 mailing lists used by pilot participants • Ego-net focus • History of events reconstructed • Basic descriptive stats Code file collaboration • Code files modified by pilot participants • History of events reconstructed • Basic descriptive stats 7
  8. 8. Methods: Activity In our (very) preliminary analysis as actor-level measures of activity we measured: Mailing lists: • Weighted degree centrality of contributors to capture their involvement in the discussion of development topics Code files: • Weighted degree centrality of contributors to capture their activity in code production 8
  9. 9. Methods: Collaboration In our preliminary analysis as actor-level measures of collaboration we measured: Mailing lists: • Number of 2-paths: to capture the amount of participation by others in development topics discussed by contributors Code files: • Number of 2-paths: to capture the amount of contribution by others to files being worked on by contributors 9
  10. 10. Results: Collaboration in the Linux kernel In person (events) Feedback on code contributions aka patches (mailing list) General mailing list discussions Feedback on bugs (mailing list) Working on same code file(s) 10
  11. 11. Time (Weeks) WeightedDegree 0 10 20 30 40 50 60 0102030405060 1 2 3 4 Mailing Lists Results: Weighted Degree Centrality Code files 11 Time (Weeks) WeightedDegree 0 10 20 30 40 50 60 01000200030004000 1 2 3 4
  12. 12. Mailing Lists Results: Two-Path Code files Time (Weeks) Two−paths 0 10 20 30 40 50 60 020406080100120 1 2 3 4 12 Time (Weeks) WeightedDegree 0 10 20 30 40 50 60 05000100001500020000 1 2 3 4
  13. 13. Implications and Relevance Collaboration is multiplex in the eyes of the contributors The inspection of activity and (potential) collaboration in mailing lists and code show complementary pictures Ability to identify contributors and their actions across multiple activities of code production is paramount if we want to study the structuring of collaboration 13
  14. 14. Discussion and Future Work Face-to-face collaboration: how to capture it? Identities across multiple online repositories Validation 14
  15. 15. Thank You and Questions Authors: Dawn M. Foster dawn@dawnfoster.com Guido Conaldi G.Conaldi@greenwich.ac.uk Riccardo De Vita R.DeVita@greenwich.ac.uk University of Greenwich, Centre for Business Network Analysis 15
  16. 16. References Data on Linux kernel contributions: • Corbet, J., Kroah-Hartman, G. & McPherson, A., 2015. Linux Kernel Development: How Fast is it Going, Who is Doing It, What Are They Doing and Who is Sponsoring the Work, Available at: http://www.linuxfoundation.org/publications/linux-foundation/who- writes-linux-2015. Literature: • Crowston, K., & Scozzi B. (2008). Bug Fixing Practices within Free/Libre Open Source Software Development Teams. Journal of Database Management. 19(2), 1–30. • Mockus, A., Fielding, R.T. & Herbsleb, J.D., 2002. Two case studies of open source software development: Apache and Mozilla. ACM Transactions on Software Engineering and Methodology, 11(3), pp. 309–346. • Conaldi, G., Lomi, A. & Tonellato, M., 2012. Dynamic models of affiliation and the network structure of problem solving in an open source software project. Organizational Research Methods, 15(3), pp. 385–412. • Von Krogh, G., Spaeth, S., & Lakhani, K. R., 2003. Community, joining, and specialization in open source software innovation: a case study. Research Policy, 32(7), pp. 1217-1241.
  17. 17. Backup 18
  18. 18. Mailing Lists Results: Two-Path Repeated Code files Time (Weeks) Repeatedtwo−paths 0 10 20 30 40 50 60 051015 1 2 3 4 Time (Weeks) Repeatedtwo−paths 0 20 40 60 020406080100120 1 2 3 4 19

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