human migration of open- source contributors kick-off presentation erik kouters graduation...

Human Migration of Open-Source Contributors

Kick-off Presentation

Erik Kouters

Graduation supervisor: A. Serebrenik

Graduation tutor: B. Vasilescu

What have I done so far?

• Geographical Movement of Mailing List Participants• Seminar SET

• Capita Selecta SET

• Who’s who in GNOME: using LSA to merge software repository identities• ICSM 2012 ERA Track

/ Software Engineering & Technology PAGE 218-04-23

What are the main topics?

• Human migration of open-source contributors• Identity matching

• Case study: GNOME


Why is human migration of open-source contributors interesting?

• A passionate contributor would visit a conference.

• Don't program on Fridays!• Contributors that appear as weekend commuters are

less likely to introduce bugs on Fridays.

• Translators that reside in a different country than the country of the target language are expected to deliver translations of lower quality.


What’s so interesting about this human migration of open-source contributors?

• What (geographical) patterns does the migration of open-source contributors follow?• Which patterns (source destination) are most

popular?

− Commute

− Conferences

• What are the factors that influence this migration?• Which factors are most influential?


How am I planning to trace these migrations?

• Extract emails from mailing list archive• Resolve emails to location• Email A is sent from locationA at timestampA• Email B is sent from locationB at timestampB• <locationA, timestampA> + <locationB, timestampB>

= migration!

• But what if the contributor uses multiple email addresses?


What exactly is Identity Matching?

• Identifying which aliases belong to the same individual

• Common in the form <name, emailAddress>

• <“George Stefanakis”, george.stefanakis@domainA>• <“Stephanakis, George”, g.stephanakis@domainB>

• Needs some similarity measure (e.g. edit distance)


mailto:george.stefanakis@domainA

mailto:g.stephanakis@domainB

How am I going to match these identities?


What will I be doing to improve the identity matching?

• Increase confidence when merging email addresses• Look at fellow recipients (mailing list)• Look at coauthors (source code repository)

• Use multiple similarity measures• Currently Levenshtein and Cosine Similarity• Compare performance with others (e.g. Jaccard, Jaro-Winkler,

Dice’s coefficient, etc.)

• Improve implementation• Currently slow• Data set limited to system’s memory

• Release the tool as open-source (e.g. Github)• Compare to current implementations


So, what will I be doing?

1. Improve the identity matching algorithm’s performance

2. Run the algorithm on the data from the mailing list archive

3. Send out a questionnaire to verify the results

4. While waiting for the questionnaire, improve the algorithm with more advanced techniques

5. When we have received sufficient responses on questionnaire, analyse the data and look for patterns


A questionnaire? What about privacy?

• Only the individual can access the data• Participation by entering their email address

• Unique URL (hash) mailed to the email address

• Data will not be made public• Research published based on the data will be

anonymised


How do I confirm the identity matching?


How do I confirm the migrations?


Looks promising…


And what am I hoping to achieve?

• A more advanced and better performing identity matching algorithm than currently exists• Versatile and open-source tool

• According to which patterns and why skilled workers (open-source contributors) migrate• Work during holiday Hobbyist

• Visits conferences High activity in project

• More publications!


Thank you!

• Questions?


human migration of open- source contributors kick-off presentation erik kouters graduation...

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