This document studies the impact of developer reputation on code review outcomes in open source software projects. It analyzes code review data from 8 open source projects to test hypotheses about whether core developers, who have higher reputation, have shorter feedback and review intervals, higher acceptance rates, and fewer required patchsets than peripheral developers. The results provide empirical evidence that core developers do receive preferential treatment in code reviews, supporting all the hypotheses. This suggests reputation influences code review behaviors and outcomes for developers in open source projects.

1. Impact of Developer Reputa1on on Code
Review Outcomes in OSS Projects:
An Empirical Inves1ga1on
Amiangshu
Bosu,
Jeffrey
C.
Carver
Department
of
Computer
Science
University
of
Alabama

2. Open Source SoAware Communi1es
2
What
is
the
moAvaAon
to
contribute?
ReputaAon
among
teammates

3. Problem
3
Project
ReputaAon
Effort
Expended
Contribute
PosAng
Defects
Mailing
List
Fixing
Bugs
User
Support
Code
Review
Code
Changes

4. Research Goal
4
Project
ReputaAon
Effort
Expended
Contribute
PosAng
Defects
Mailing
List
Fixing
Bugs
User
Support
Code
Review
Code
Changes

5. Measuring Reputa1on
5
2
1
9
0
8
7
5
6
4
3

6. Code Review Process
6
Writes
Code
Repository
Reviews
Code
Edits
Code
Reviews
Code
…
Abandon
Merge
Approval
Review
Request
Accept

7. Hypotheses
8
H1:
Core
developers
have
shorter
first
feedback
interval
than
peripheral
developers
Writes
Code
Repository
First
Feedback
Reviews
Code
Edits
Code
H2:
Core
developers
have
shorter
review
interval
than
peripheral
developers
Reviews
Code
…
Abandon
Merge
Approval
Review
Request
Accept
Interval
Review
Interval
H4:
Core
developers
are
able
to
get
change
accepted
with
fewer
patchsets
than
peripheral
developers
H3:
Core
developers
have
higher
acceptance
rate
than
peripheral
developers

8. Detec1ng Core and Peripheral Developers
9
BorgaW-­‐EvereY
Algorithm

9. Detec1ng Core and Peripheral Developers
10
BorgaW-­‐EvereY
Algorithm
MulA-­‐Core
Networks
Developed
new
approach

10. Detec1ng Core and Periphery Developers
11
Centrality
Measures
1
2
0
8
9
7
6
4
5
3
Core
Developers
Degree
Betweenness
Closeness
Eigenvector
Pagerank
Eccentricity
Combine
with
K-­‐
means
clustering
algorithm
CIK
Method

11. Valida1on of CIK
12
1. Correctly
classifies
nodes
from
BorgaW-­‐
EvereY
example
2. %
of
core
members
idenAfied
consistent
with
prior
OSS
studies
3. %
code
contribuAons
by
core
members
is
consistent
with
prior
OSS
studies
4. Visual
ValidaAon

12. Results
13

13. Project Demographics
14
Project
Domain
Technology
Number
of
code
reviews
Chromium
OS
OperaAng
System
C,
C++
49,855
ITK/VTK
VisualizaAon
Toolkit
C++
13,207
LibreOffice
Office
applicaAon
suite
C++
6,347
OmapZoom
Mobile
development
plaiorm
C
32,930
OpenStack
Cloud
compuAng
Python,
JavaScript
59,125
Ovirt
Virtual
machine
management
Java
23,316
Qt
UI
Framework
C,
C++
71,732
Typo3
Content
management
system
PHP,
JavaScript
24,374

14. H1: First Feedback Interval
15
All
Differences
Significant

15. H1: Experience vs. First Feedback Interval
16

16. H2: Review Interval
17
All
Differences
Significant

17. H2: Experience vs. Review Interval
18

18. H3: Acceptance Rate
19

19. H3: Acceptance Rate vs. Experience
20

20. H4: Number of Patchsets
21

21. Results Summary and Discussion
23
H1:
Core
developers
have
shorter
first
feedback
interval
than
peripheral
developers
Writes
Code
Repository
First
Feedback
Reviews
Code
Edits
Code
H2:
Core
developers
have
shorter
review
interval
than
peripheral
developers
Reviews
Code
…
Abandon
Merge
Approval
Review
Request
Accept
Interval
Review
Interval
H4:
Core
developers
are
able
to
get
change
accepted
with
fewer
patchsets
than
peripheral
developers
H3:
Core
developers
have
higher
acceptance
rate
than
peripheral
developers

22. Contribu1ons
25
Code
Review
Social
Network
ReputaAon
Code
Review
CIK
Method

23. Impact of Developer Reputa1on on Code
Review Outcomes in OSS Projects:
An Empirical Inves1ga1on
asbosu@ua.edu,
carver@cs.ua.edu
hMp://asbosu.students.cs.ua.edu/sna-­‐code-­‐review/