Developer Identification Methods for Integrated Data from Various Sources Gregorio Robles Jesus M. Gonzalez-Barahona

1
Developer Identification Methods for Integrated
Data from Various Sources Gregorio Robles Jesus
M. Gonzalez-Barahona

• Presented by
• Brian Chan
• Cisc 864

2
Table of Contents

• Background Information
• Problems Addressed
• Motivation
• Data Gathered
• Conclusion
• Personal Thoughts
• Question and Comments

3
Background Information

• Data mining for project comes from a single
  source of data
• Results can be applied to Libre Software
• Look at separately
• Mailing Lists
• Bug Repositories

4
Background Information

• Libre Software shows Pareto law for commits
• For each major artifact, 20 of developers are
  shown to contribute 80 of the activity in it.

5
Problems Addressed

• Are the people that commit so much in one
  artifact the same people in the other artifact?
• People use different identities in each artifact
• Current mining techniques focus on one artifact
  so cannot tell who is who

6
Motivation

• To gain insight into the social network and
  structure of libre software projects
• To find all the identities that correspond to one
  person
• Focus more on data analysis rather than the
  extraction process

7
Data Gathered

• Actor has access to Figure 1.0
• artifacts
• Alternate rules for
• each artifact

8
Data Gathered

• Actor can post on more
• than one mailing list
• bylchan_at_ca.ibm.com
• briancha_at_ca.ibm.com
• Source Files can appear with many
• identities Brian Chan
• Brian
• bchan
• Interaction with versioning repository occurs
  through account in server machine
• Bug tracking systems require email address i.e.
  Bugzilla

9
Data Gathered

• Primary Figure 2.0
• Required Information
• Secondary
• Not Required
• for the transaction
• i.e. name in email

10
Data Gathered (contd)

• Automated process extracts data into data
  repository
• Figure 3.0

11
Data Gathered

• Sources Table
• Lists where id information was originally
  extracted i.e. file1.C
• bugreport230
• Identification Table
• Identity
• Id key to Source table

12
Data Gathered

• Persons
• Gender, Nationality, Hash
• Identifications
• Pseudo identity bchan
• Match number with another identity
• Matches
• Tells which two identities belong to the same
  actor
• Table 1.0

1 bchan bylchan_at_ca.ibm.com Deduction 80
1 Brian Chan bylchan_at_ca.ibm.com Same Email 90
13
Data Gathered

• Matching during automated data gathering process
• Inference
• Automatic Heuristics
• Human Verification

14
Data Gathered

• Rule 1
• Primary Identities may have part of the real name
  in it
• Example User ltusername_at_example.com
• Rule 2
• Identities can be built from another one
• nsurname_at_example.com, name.surname_at_example.com
• name surname_at_example.com
• Rule 3
• Some projects or repositories have foresight to
  keep list information that can be used for
  matching

15
Data Gathered

• Still error in matching algorithms but in
  statistical gathering process, if it is small
  enough then can be ignored.
• Still use cleaning and verification.

16
Data Gathered

• Privacy Issues
• Use Hash value (1st Firewall) to reference
  information. Cannot reference Identifications
  directly
• Person ID (2nd Firewall) Given in such a way so
  cannot infer real identity without direct access
  to Identifications table
• Given to unique person so hackers cannot find
  specific id

17
Conclusions

• Actors in Libre Software may use many different
  identities for development
• Paper deals with design of how to account for all
  the different people and who is actually doing
  what
• Discussed how privacy can be dealt with

18
Personal Thoughts

• Good Points
• Effective Solution
• Good examination of all the different identities
  in business
• Unique interpretation of data mining

19
Personal Thoughts

• Points for improvement
• No actual data to view results
• Reference GNOME but never actually give
  statistical information from it
• Some interpretation is left to the reader

20
Questions and Comments