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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
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
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
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
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
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
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
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
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
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
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
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
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
Discussion and Future Work
Face-to-face collaboration: how to capture it?
Identities across multiple online repositories
Validation
14
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
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.
Backup
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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Operationalisation of Collaboration Sunbelt 2015

  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Discussion and Future Work Face-to-face collaboration: how to capture it? Identities across multiple online repositories Validation 14
  • 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.
  • 17. 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.
  • 19. 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