Social and Technical Evolution
of Software Ecosystems
A Case Study of Rails
Eleni Constantinou, Tom Mens
4th International Workshop on Software
Ecosystem Architectures (WEA 2016)

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Research
Team

Introduction
Software ecosystem
• Collection of software projects that are developed and evolve together
in the same environment [1]
Ecosystem environment
• Development team ⇒ Social aspect
• Source code artefacts ⇒ Technical aspect
Modifications
• Social: Contributors joining/leaving
• Technical: New/obsolete source code files
[1] M. Lungu. Towards reverse engineering software ecosystems. Int'l Conf. Software Maintenance, pages 428-431, 2008. 2

Introduction
Evolution
• Longevity
• Growth
Ecosystem sustainability
Negative impact of major social changes
A sustainable software ecosystem can
increase or maintain its user/developer
community over longer periods of time
and can survive inherent changes
such as new technologies or new
products (e.g. from competitors) that can
change the population (the community
of users, developers etc) [2]
[2] D. Dhungana, I. Groher, E. Schludermann, S. Biffl. Software ecosystems vs. natural ecosystems: learning from the ingenious mind of nature. Eur.
Conf. on Software Architecture: Companion Volume, pages 96-102, 2010. 3

Background
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Time
Unit 1
Time
Unit 2
Time
Unit 3
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Time
Unit N-2
Time
Unit N-1
Time
Unit N
S
T
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Software Ecosystem Evolution
Technical
Artefacts
Technical
Artefacts

Definitions
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Social Metrics
Leavers(t)
Joiners(t)
Stayers(t)
TeamTurnover(t)
TeamAbandonment(t)
Technical Metrics
Obsolete(t)
New(t)
Maintained(t)
FileTurnover(t)
FileAbandonment(t)

Dataset
• Ruby on Rails
• Largest/most popular Ruby project
• GHTorrent dataset [2] (2016-09-05 dump)
• Timespan: April 2008 – September 2016
• Time unit: year quarters
• Commit activity
• Base project/Forks/Ecosystem
[2] G. Gousios. The GHTorrent dataset and tool suite. Working Conf. Mining Software Repositories, pages 233-236, 2013. 6

Dataset Problems - Noise
• Forks can be simple copies of the base project
• Non source code files or irrelevant files can be committed
(e.g., temporary files)
• One-time and occasional contributors
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Dataset Filters
1. Forks
Filter: Merged back to the base
2. Files
Filter: Source code files
3. Contributors
Filter: Contributors whose AVG activity
is equal/greater than 2 quarters
Base Forks Ecosystem
Count 1 1,896 1,897
Contributors 1,827 2,154 3,121
Commits 43,195 25,938 69,133
Base Forks Ecosystem
Count 1 692 693
Contributors 430 681 765
Commits 40,660 22,923 63,583
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Research Questions
RQ1 How does the commit activity of the ecosystem
(in base and forks) evolve over time?
RQ2 How does the development population and file activity
change over time?
RQ3 How do changes in the development team affect the file
activity of the ecosystem?
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RQ1 How does the commit activity of the ecosystem
(in base and forks) evolve over time?
Forks
since quarter 13 (July 2011)
• Increasing commit activity
• Development effort heavily
depends on forks since
October 2012 (quarter 18)
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RQ2 How does the development population and file
activity change over time?
Base Project Forks Ecosystem
Core contributors: Small number of people join/leave the
ecosystem
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RQ2 How does the development population and file
activity change over time?
Base Project Forks Ecosystem
Forks: Increasing trend
Low number of obsolete files 12

RQ2 How does the development population and
file activity change over time?
Percentage %
TeamTurnover 25 ± 12
TeamAbandonment 14 ± 10
FileTurnover 15 ± 11
FileAbandonment 10 ± 7
Moderate social and technical modifications
Ecosystem growth
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RQ3 How do changes in the development team affect the
file activity of the ecosystem?
25% of obsolete files were
maintained by Leavers
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Findings
• Intensive use of the fork and push mechanisms
of GitHub since July 2011 (quarter 13)
• Both the development team and files showed
a roughly linearly increasing trend
• Moderate impact of Leavers on the technical part
of the ecosystem
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Do Leavers engage in other ecosystems?
Ecosystem Active in Ruby
JavaScript 18,038
Python 10,211
Java 7,363
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Ecosystem Abandoned Ruby Percentage
JavaScript 13,814 77%
Python 8,131 79%
Java 5,132 70%

Threats to validity
Multiple user accounts
• Less common within the same GitHub
repository
• Identity merging [3]
Rails project
• Large/significant Ruby project
• Entire Ruby ecosystem
Effort measurement
• Commit squashing
• LOC
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[3] M. Goeminne and T. Mens, “A comparison of identity merge algorithms for software repositories,” Science of Computer Programming, vol. 78, no. 8,
pages 971–986, 2013

Conclusion
• Case study of the Rails evolution in GitHub
• Magnitude and effect of socio-technical changes
• Moderate impact of modifications on the ecosystem
• Sustainable ecosystem
• Socio-technical growth
• Longevity
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Ongoing/Future Work
• Ruby ecosystem in GitHub (>60K projects)
• Leavers knowledge and specialization (relative entropy)
• Ecosystem migration (Ruby à JavaScript)
• Practices eliminating the effect of occasional contributors
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Thank you!
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