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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ResearchTeam

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IntroductionSoftware 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.

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IntroductionEvolution• 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.

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Background

Time Unit 1

Time Unit 2

Time Unit 3 … Time

Unit N-2Time

Unit N-1Time

Unit NSTART

END

Software Ecosystem Evolution

Technical Artefacts

Technical Artefacts

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Definitions

Social Metrics

Leavers(t)

Joiners(t)

Stayers(t)

TeamTurnover(t)

TeamAbandonment(t)

Technical Metrics

Obsolete(t)

New(t)

Maintained(t)

FileTurnover(t)

FileAbandonment(t)

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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.

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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. ForksFilter: Merged back to the base

2. FilesFilter: Source code files

3. ContributorsFilter: 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• >= quarter 13 (July 2011)

• Increasing commit activity• Development effort heavily

depends on forks after quarter 18 (October 2012)

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 trendLow number of obsolete files

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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 modificationsEcosystem 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 from

quarter 13 (July 2011)

• 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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FindingsDo Leavers engage in other ecosystems?

Ecosystem Active in Ruby

JavaScript 18,038

Python 10,211

Java 7,363

Ecosystem Abandoned Ruby Percentage

JavaScript 13,814 77%

Python 8,131 79%

Java 5,132 70%

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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

[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

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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 -> JavaScript)

• Practices eliminating the effect of occasional contributors

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Thank you!

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