Network Relationships and Job Changes of Software Developers

Dawn M. Foster, Guido Conaldi, Riccardo De VitaSunbelt XXXVI April 6, 2016

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

The Context

Sunbelt 2015: Operationalizing Collaboration

Another pilot for 2016: Job changes

3

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

The Challenge

Open source software is a collaborative effort

People from many organizations work together

Risk of Job Changes (influence)

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, 1,200 orgs

6 * Corbet et al., 2015

Job Changes

Influence moves with an actor to a new job

Job change data

Activity in code production and mailing list.

7

Approach: Event History AnalysisCox 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

Pilot DataComplete 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

10i A

Mailing list posts

Day 0

11i A

i B

j B

j C

k A

i A

j A

60 days of past mailing list postsi A

Day 0

Past 30 days

Past 60 days

12i A

i B

j B

j C

k A

i A

j A

60 days of past mailing list posts: Degreei A

Day 0

Past 30 days

Past 60 days

13i A

i B

j B

j C

k A

i A

j A

60 days of past mailing list posts: Two-Pathi A

Day 0

Past 30 days

Past 60 days

14i A

i B

j B

j C

k A

i A

j A

60 days of past mailing list posts: Four-Cyclei A

Day 0

Past 30 days

Past 60 days

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

Limitations and Future Work

More data

Other time windows

Missing covariates

Additional network analysis

16

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

ReferencesCorbet, 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.