This document outlines a research project focused on the collaboration patterns of paid software developers within the Linux kernel community, exploring the impact of job changes on these relationships. It employs a quantitative approach through event history analysis to assess the factors influencing code contributions among developers who have changed jobs. The study highlights how paid contributions may enhance the overall dynamics and innovation within open-source projects.

1. Network Relationships and Job Changes of Software Developers
Dawn M. Foster, Guido Conaldi, Riccardo De Vita
Sunbelt XXXVI April 6, 2016

2. The Context
Part of Larger Research Project - PhD Dissertation
• Qualitative: how participants talk about
collaboration
• Network Analysis: quantify collaboration
Research Question for Overall Research Project:
• How do participants who are paid by
organizations collaborate within a community?
2

3. The Context
Sunbelt 2015: Operationalizing Collaboration
Another pilot for 2016: Job changes
3

4. Literature on paid developers in open source
• Organizations and individuals devote time and resources to contribute to
public good projects (Grand et al 2004; von Hippel & von Krogh 2003).
• Paid software developers are one way that organizations contribute, and
projects are seeing increased participation from paid developers
(Jensen & Scacchi 2007; Roberts et al. 2006).
• Those who are paid to contribute are often motivated to make more
contributions (Roberts et al. 2006), have a stronger desire to get their
code into the project, and some move into leadership roles (Shah 2006).
• While some researchers emphasize the antagonism between corporate
and community interests (Hars & Ou 2002), others have found benefits
to organizations in open source communities for innovation, knowledge
creation, etc. (Henkel 2006; Mockus et al. 2002).
4

5. The Challenge
Open source software is a collaborative effort
People from many organizations work together
Risk of Job Changes (influence)
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, 1,200 orgs
6 * Corbet et al., 2015

7. Job Changes
Influence moves with an actor to a new job
Job change data
Activity in code production and mailing list.
7

8. Approach: Event History Analysis
Cox Regression Model (Proportional Hazard)
h(t) = h0(t) exp(β’x)
• Hazard function of time
• h0(t) - baseline hazard function
• β’x – covariates that affect hazard rate
• set of x’s - variable of interest over vector of β’s
• For each estimated β, exp(β) gives respective hazard ratio
Pilot: Validate approach with subset of collaboration data
• Future: expand and run model of more complete data set.
8

9. Pilot Data
Complete Job Change Data:
• 2006-03-20 through 2015-11-01
• 414 people changed jobs; 661 job changes
Pilot Data: Subset of Mailing List Posts
• Selected 10 of the most widely used mailing lists (240+ lists)
• People with posts from these lists: 195
• Job changes: 239
• Post spells with posts: 80,724
9

10. 10
i A
Mailing list posts
Day 0

11. 11
i A
i B
j B
j C
k A
i A
j A
60 days of past mailing list posts
i A
Day 0
Past 30 days
Past 60 days

12. 12
i A
i B
j B
j C
k A
i A
j A
60 days of past mailing list posts: Degree
i A
Day 0
Past 30 days
Past 60 days

13. 13
i A
i B
j B
j C
k A
i A
j A
60 days of past mailing list posts: Two-Path
i A
Day 0
Past 30 days
Past 60 days

14. 14
i A
i B
j B
j C
k A
i A
j A
60 days of past mailing list posts: Four-Cycle
i A
Day 0
Past 30 days
Past 60 days

15. Results and Implications
15
Variable exp(coef) Robust SE Significance
Code Commits 0.964546*** 0.010149 (0.000376)
Degree 0.952422 0.025764 (0.058478)
2-Path 1.022684 0.032780 (0.493801)
4-Cycle 0.726703** 0.104016 (0.002147)
Primary Email 0.678074* 0.160710 (0.015632)
N. of observations: 80724
N. of events: 239
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

16. Limitations and Future Work
More data
Other time windows
Missing covariates
Additional network analysis
16

17. Thank You and Questions
Authors:
Dawn M. Foster D.M.Foster@greenwich.ac.uk
@geekygirldawn http://fastwonderblog.com
Guido Conaldi G.Conaldi@greenwich.ac.uk
Riccardo De Vita R.DeVita@greenwich.ac.uk
University of Greenwich, Centre for Business Network Analysis
http://www.gre.ac.uk/business/research/centres/cbna/
17

19. References
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.
Grand, S., von Krogh, G., Leonard, D. & Swap, W., 2004. Resource allocation beyond firm boundaries.
Long Range Planning, 37(6), pp.591–610.
Von Hippel, E. & von Krogh, G., 2003. Open Source Software and the “Private-Collective” Innovation
Model: Issues for Organization Science. Organization science, 14(2), pp.209–223.
Jensen, C. & Scacchi, W., 2007. Role Migration and Advancement Processes in OSSD Projects: A
Comparative Case Study. 29th International Conference on Software Engineering (ICSE’07), pp.364–
374.
Roberts, J., Hann, I. & Slaughter, S., 2006. Understanding the motivations, participation, and performance
of open source software developers: A longitudinal study of the Apache projects. Management science,
52(7), pp.984–999.
Shah, S.K., 2006. Motivation, Governance, and the Viability of Hybrid Forms, in Open Source Software
Development. Management Science, 52(7), pp.1000–1014.
Hars, A. & Ou, S., 2002. Working for free? Motivations of participating in open source projects.
International Journal of Electronic Commerce, 6(3), pp.25–39.
Henkel, J., 2006. Selective revealing in open innovation processes: The case of embedded Linux.
Research Policy, 35, pp.953–969.
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.