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1
The Anatomy of Developer Social Networks
Qiaona HONGSupervisor: Prof. Shing-Chi Cheung
2
Social Network• Study the Topological Structure of Social
Network– Y. Y. Ahn @WWW '07; A. Mislove@IMC '07
• Study the Community Structure of Social Network– V. D. Blondel@ Journal of Statistical Mechanics:
Theory and Experiment; Y. R. Lin@WI '07• Techniques to visualize the social network
– Jeffrey Heer@InfoVis '05• Influential People & Information Diffusion
– Kimura, M.@InfoVis '07• Friend Recommendation
– Nitai B. Silva@WCCI‘10
General Social Network (GSN)
3
Research Questions
• Q1: What are the similarities and differences between DSNs and GSNs?
4
Research Questions
• Q1: What are the similarities and differences between DSNs and GSNs?
• Q2: How do DSNs evolve over time?• Q3: How do communities evolve in DSNs?• Q4: What are the similarities and differences
between DSNs extracted using different social linkage indicators?
5
Research Questions
• Q1: What are the similarities and differences between DSNs and GSNs?
• Q2: How do DSNs evolve over time?• Q3: How do communities evolve in DSNs?• Q4: What are the similarities and differences
between DSNs extracted using different social linkage indicators?
Qiaona HONG, Sunghun Kim, S.C. Cheung and Christian Bird, “Understanding a Developer Social Network and its Evolution”, in Proceedings of the 27th IEEE International Conference on Software Maintenance, 2011.
6
Subjects
• Mozilla Bug Report: 2000-2009 – 496,692 bug reports– 3,893,025 comments
• Mozilla CVS Log: 2000-2009– 44394 revisions
• Eclipse Bug Report: 2002-2009– 294,938 bug reports– 1,618,667 comments
• Eclipse CVS Log: 2002-2009– 22493 revisions
7
DSN Extraction Approach
David Comment 1
Bug Report 1
Jack Comment 3
Bob Comment 2
Bob Comment 1
Bug Report 2
Bill Comment 3
Jack Comment 2
Bug Report 4
Jack Comment 3
Bob Comment 2David Comment 1
Bug Report 3
Bill Comment 3
Bob Comment 2
Jack Comment 3
David Bill
Bob Jack
8
DSN Extraction Approach
David Comment 1
Bug Report 1
Jack Comment 3
Bob Comment 2
Bob Comment 1
Bug Report 2
Bill Comment 3
Jack Comment 2
Bug Report 4
Jack Comment 3
Bob Comment 2David Comment 1
Bug Report 3
Bill Comment 3
Bob Comment 2
Jack Comment 3
David Bill
Bob Jack
1
2 2
4
2 2
9
DSN Extraction Approach
David Comment 1
Bug Report 1
Jack Comment 3
Bob Comment 2
Bob Comment 1
Bug Report 2
Bill Comment 3
Jack Comment 2
Bug Report 4
Jack Comment 3
Bob Comment 2David Comment 1
Bug Report 3
Bill Comment 3
Bob Comment 2
Jack Comment 3
David Bill
Bob Jack
4
10
DSN Extraction Approach
David Comment 1
Bug Report 1
Jack Comment 3
Bob Comment 2
Bob Comment 1
Bug Report 2
Bill Comment 3
Jack Comment 2
Bug Report 4
Jack Comment 3
Bob Comment 2David Comment 1
Bug Report 3
Bill Comment 3
Bob Comment 2
Jack Comment 3
Bob Jack
11
Metrics
• Degree Distribution– The number of edges connected to a node
• Degree of Separation– The shortest path between two nodes
• Modularity– To measure the quality of division of nodes
• Community Size– The number of nodes within a community
12
Modularity
A B0.51 0.176
• According to A. Clauset’s work, modularity of 0.3 is a good indicator of significant community structure in a network
• When the modularity is 0, the community structure is no stronger than that of a randomly generated network
13
Communities in DSN
• Identified Communities in DSN– Louvain Algorithm (by optimizing modularity)– 50 different input ordering of nodes
14
?Q1: What are the similarities
and differences between DSNs and GSNs
Degree of Distribution Degree of Separation
Modularity Community Size
15
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Q1: What are the similarities and differences between DSNs and GSNs
16
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Q1: What are the similarities and differences between DSNs and GSNs
17
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
• Quantitative power law fit test– An approach of analyzing power law distributed
data introduced by A. Clauset et al.
• P-value : The likelihood that degree distribution does actually follow a power-law– If p-value is less than 0.1, the power law is
rejected.
Q1: What are the similarities and differences between DSNs and GSNs
18
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
P-value<0.1 some<0.1,other>0.1
Different from GSNs, DSNs do not follow power-law
Q1: What are the similarities and differences between DSNs and GSNs
19
Degree of Separation
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 180.
00.
20.
40.
60.
81.
0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
Q1: What are the similarities and differences between DSNs and GSNs
20
Degree of Separation
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 180.
00.
20.
40.
60.
81.
0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
4.12
90% (6)
Q1: What are the similarities and differences between DSNs and GSNs
21
Degree of Separation
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 180.
00.
20.
40.
60.
81.
0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of SeparationP
roba
bilit
y
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
MozillaDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-CL
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-BR
0 2 4 6 8 10 12 14 16 18
0.0
0.2
0.4
0.6
EclipseDSN-CL
Degree of Separation
Pro
babi
lity
Distance between two developers
1-month DSN3-month DSN6-month DSN
1-year DSN2-year DSN4-year DSN
tw itter(8000 sample)cyw orld(3000 sample)
3.0 2.1
4.0 2.5
Developers in DSN are much closer to each other than participants in GSN.
Q1: What are the similarities and differences between DSNs and GSNs
22
Modularity
Network
Mod
ular
ity
0.3
0.4
0.5
0.6
0.7EclipseDSN-BR
0.3
0.4
0.5
0.6
0.7
EclipseDSN-CL
0.3
0.4
0.5
0.6
0.7
MozillaDSN-BR
0.3
0.4
0.5
0.6
0.7MozillaDSN-CL
Modularity
Similar to GSNs, all DSNs have significant community structure
Q1: What are the similarities and differences between DSNs and GSNs
23
Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Q1: What are the similarities and differences between DSNs and GSNs
24
Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
28%
Q1: What are the similarities and differences between DSNs and GSNs
25
Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
21%-36% 23%-43%
15%-30% 23%-33%
Q1: What are the similarities and differences between DSNs and GSNs
26
?Q4:What are the similarities and differences between DSNs extracted using different social linkage indicators
Q2: How do DSNs evolve over time?
Degree of Distribution Degree of Separation
Modularity Community Size
27
Change of Developer Size
DSNs-BR always have more developers than DSNs-CL
Q2: How do DSNs evolve over time?
28
Change of Percentage of New Comers
DSNs-BR always have higher percentage of new comers than DSNs-CL
Q2: How do DSNs evolve over time?
29
Change of Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2000fh2000sh2001fh2001sh2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2000fh2000sh2001fh2001sh2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
Q2: How do DSNs evolve over time?
30
Change of Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2000fh2000sh2001fh2001sh2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2000fh2000sh2001fh2001sh2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
Cumulative Degree Distribution Evolution
Degree
Pro
ba
bili
ty(d
eg
ree
>=
x)
100
101
102
103
104
10-4
10-3
10-2
10-1
100
2002fh2002sh2003fh2003sh2004fh2004sh2005fh2005sh2006fh2006sh2007fh2007sh2008fh2008sh2009fh2009sh
14(20) DSNs <0.1
15(16) DSNs <0.1
DSNs which has p-value <0.1 have
bigger size
DSNs do not follow power law degree distribution over time
Q2: How do DSNs evolve over time?
31
Change of Degree of Separation
0 3 6 9 12 0 3 6 9 12 0 3 6 9 12 0 3 6 9 12
Degree of Separation Evolution
Distance between two developers
Pro
ba
bili
ty
0.0
0.2
0.4
0.6
0.8
1.0
MozillaDSN-BR MozillaDSN-CL EclipseDSN-BR EclipseDSN-CL
2000fh2000sh2001fh2001sh
2002fh2002sh2003fh2003sh
2004fh2004sh2005fh2005sh
2006fh2006sh2007fh2007sh
2008fh2008sh2009fh2009sh
2.45-3.02 2.21-3.91 2.54-3.97 2.22-3.71
Developers in DSN are always closer to each other over time
Q2: How do DSNs evolve over time?
32
Modularity Evolution
Time
Mod
ular
ity
0.2
0.4
0.6
2000
f
2000
s
2001
f
2001
s
2002
f
2002
s
2003
f
2003
s
2004
f
2004
s
2005
f
2005
s
2006
f
2006
s
2007
f
2007
s
2008
f
2008
s
2009
f
2009
s
EclipseDSN-BR
0.2
0.4
0.6
EclipseDSN-CL
0.2
0.4
0.6
MozillaDSN-BR
0.2
0.4
0.6
MozillaDSN-CL
Modularity
There are clear increases in modularity over time for MozillaDSN-CL, MozillaDSN-BR, EclipseDSN-BR
Q2: How do DSNs evolve over time?
33
Modularity Evolution
Time
Mod
ular
ity
0.2
0.4
0.6
2000
f
2000
s
2001
f
2001
s
2002
f
2002
s
2003
f
2003
s
2004
f
2004
s
2005
f
2005
s
2006
f
2006
s
2007
f
2007
s
2008
f
2008
s
2009
f
2009
s
EclipseDSN-BR
0.2
0.4
0.6
EclipseDSN-CL
0.2
0.4
0.6
MozillaDSN-BR
0.2
0.4
0.6
MozillaDSN-CL
Modularity
0.3
0.3
0.3
0.3
All DSNs tend to have stronger community structure over time
Q2: How do DSNs evolve over time?
34
Change of Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Q2: How do DSNs evolve over time?
35
Change of Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
Q2: How do DSNs evolve over time?
36
? Q3:How do communities evolve in DSNs?
Six patterns
Expand Shrink
Merge Split
Extinct Emerge
Q3:How do communities evolve in DSNs?
Community evolution map
Mozilla
Eclipse
Q3:How do communities evolve in DSNs?
Community evolution map
Mozilla
Eclipse
FireFox Web Tools (WTP) Project
Q3:How do communities evolve in DSNs?
Community evolution map
Firefox 3 release
FireFox
Q3:How do communities evolve in DSNs?
41
Findings
• Comparison– DSN do not follow power law degree distribution– Developers in DSN are closer to each than people in GSN– DSN has smaller community size– Similar to GSNs, DSN has significant community structure– DSNs-BR have a higher percentage of new comers– Developers in DSNs-CL are closer
• Evolution– Over time DSN tends to have stronger and more stable
community structure– Evolution patterns reflect events happened
42
Discussion and Future Work
• Discussion– Our conclusions are only based on Mozilla and
Eclipse• Our approach could be easily applied to other projects
– Use threshold to remove casual users• Their low connectivity implies that these participants
have little impact on the properties of DSNs that we have examined
43
Discussion and Future Work
• Future Work– Apply social analysis to other open source
software development process• Peer review processes
– Xin Yang, Raula Gaikovina Kula, Camargo Cruz Ana Erika, Norihiro Yoshida, Kazuki Hamasaki, Kenji Fujiwara, and Hajimu Iida, "Understanding OSS Peer Review Roles in Peer Review Social Network (PeRSoN)," In Proceedings of the 19th Asia-Pacific Software Engineering Conference (APSEC2012)
44
Related Work
• C. Bird, A. Gourley, P. Devanbu, M. Gertz, and A. Swaminathan, "Mining email social networks," in MSR '06: Proceedings of the 2006 international workshop on Mining software repositories. New York, NY, USA: ACM Press, 2006, pp. 137-143.
• C. Bird, D. Pattison, R. D'Souza, V. Filkov, and P. Devanbu, "Latent social structure in open source projects," in Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering, ser. SIGSOFT '08/FSE-16. New York, NY, USA: ACM, 2008, pp. 24-35.
• A. Meneely, and L. Williams, "Secure Open Source Collaboration: An Empirical Study of Linus’ Law " in Computer and Communications Security, Chicago, IL, 2009, p. 453-462.
45
Acknowledgements
• Prof. Shing-Chi CHEUNG• Prof. Sunghun Kim• Dr. Christian Bird
46
Thank YouQ&A
