Social Network Analysis for Global Software Engineering: Exploring relationships from a fine-grained level @ICGSE 2013

Social Network Analysis for
Global Software Engineering
Exploring relationships from a fine-grained level
Sabrina Marczak
PUCRS, Porto Alegre, Brazil
sabrina.marczak@pucrs.br
ICGSE 2013
8th IEEE International Conference on Global Software Engineering
Bari, Italy | August 26-29, 2013
www.icgse.org
Nicole Novielli
Uniba, Bari, Italy
nicole.novielli@uniba.it

N. Novielli, S. Marczak | ICGSE 2013 | Bari,
Italy
Software Development 2

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Software Development 3

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Software Development
• Collaboration
• Coordination
• Communication
Goals
Tasks
Dependencies
Deadlines
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• Who talks with whom?
• Who receives help from whom?
• Who is aware of whom?
• Who are the experts?
• Who are the most active contributors?
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• Are the team members following the
organizational structure?
• Are the team members coordinating with
those their work is dependent on?
• Are the next builds going to fail?
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• How to answer to these questions?Social Network Analysis
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Social Network Analysis
• It provides techniques to examine the
structure of social relationships in a group
to uncover patterns of behavior and
interaction among people [Mitchell, 1969]
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Learning Goals
• When we complete this tutorial, you will
be able to:
• Understand the main concepts related to SNA
• Better understand SNA research literature
• Have basic knowledge to identify which SNA data
you need for your own research
• Run basic SNA measures and interpret the results
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Our Agenda
• [9:00-10:30] Introduction to Social
Network Analysis:Theory and Application
from Research
• [10:30-11:00] Coffee Break
• [11:00-12:30] SNA in Practice:Tools and
Hands-on Exercises
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> Introduction to SNA
• Terminology
• Representation
• Measures
• Data collection
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Terminology
Actor
Actor = Node = Vertice
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Terminology
Tie
Tie = Link = Edge
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Terminology 14
Directional
tie

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Terminology
Dyad
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Terminology
Triad
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Representation
• Sociogram
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Representation
• Matrix representation of network data
Absent
Present
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Representation
• Actors’ attributes
Role Country Work exp.
Andrew 1 1 3
Bob 1 2 3
Charles 1 1 1
David 1 2 2
Emma 1 1 1
Fynn 2 1 3
Greg 2 1 1
Hannah 2 1 1
Iris 2 1 2
John 2 2 3
Kevin 2 2 2
Lucas 2 1 2
Role
1. Tester
2. Developer
Country
1. Canada
2. Ireland
Work experience
1. 1-6 months
2. 6-12 months
3.18+ months
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Representation
• Sociogram with actors’ attributes
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Representation
• Tie weight
• Strength
• Frequency
• Etc...
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Measures
• Overall network characterization
• Network size
• Network density
• Ties statistics
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Measures
• Network size: is the number of actors in
the social network
Size: 12 actors
Size can be larger or
smaller than the team size
Herbsleb and Mockus (2003)
found that distributed
communication networks are
significantly smaller than
same-site networks
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Measures
• Network density: is the proportion of
ties that exist in the network out of the
total possible ties. It can vary from 0 to 1.
Possible ties: 12 (12-1) / 2 = 66
Hinds and McGrath (2006) found
that geographic distribution is
associated with less dense work ties
and less dense information sharing,
suggesting that social ties are not
particularly important in distributed
as compared with collocated teams
as a means of coordinating work and
improving performance
Density: 20 / 66 = 0.30
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Measures
• Ties statistics: it uses the actors’
attributes to reveal overall network
characteristics
E.g.: 5 testers and 7 developers
By counting up the number of ties
within and cross-sites, Herbsleb and
Mockus (2003) found that there is
much more frequent communication
with local colleagues in a distributed
project than with remote ones
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Damian et al. (2007) found that
notification of changes is the main
reason for communication in
requirements-centric social
networks

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Measures
• Information exchange
• Reachability
• Component
• Centrality
• Brokerage
• Cutpoint
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Measures
• Reachability: one actor is reachable by another actor if
exists any set of ties that connects both actors, regardless
of how many others fall in between them [Wasserman and Faust, 1994].
All actors are reachable
If some actors cannot
reach others, there is a
potential division in the
network and thus
information cannot reach
everyone
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Measures
• Component: indicates whether a social network is
connected.A network is connected if there is a path between
every pair of actors, otherwise it is disconnected.The actors
in a disconnected network may be partitioned in subsets
called components [Wasserman and Faust, 1994].
One component
Component test indicates
whether there is a group of
people connected to each
other and disconnected from
the remaining, while clique
test indicates whether a
subset of actors is completely
connected
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Measures
• Centrality: it serves the purpose of indicating power and
influence in a network
Who has more power and is,
as a consequence, more
influential or more important
in our example?
Hard to say by a naked eye!

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Measures
And now: which actor has more power
in each network?
Andrew Bob
Charles
David
Emma
Greg
Fynn
(a) Star
(b) Circle
Andrew
Bob
Charles
David
Emma
Greg
Fynn
Andrew Bob Charles David Emma GregFynn
(c) Line

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Measures
• Centrality can be measured in different ways, each with
different implications
• Degree
• Closeness
• Betweeneess

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Measures
• Degree centrality: indicates the number of ties of a a certain
actor.When the ties are directional, we have out-degree which
are the ties from a certain actor to others and in-degree which
are the ties from others to a certain actor [Freeman and colleagues, 1979].
Fynn is the member with the
highest out- and in-degree
Hossain et al. (2006) found that
highly centralized members
coordinate better than others
Bird et al. (2006) found that
degree centrality indicated that
developers who actually
committed changes played much
more significant roles in the email
community than non-developers
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Measures
• Degree centrality: if we go back to the star network, one
can see that all lines of communication lead to Andrew at the
center of the network
What can we conclude from this
example? Is Andrew the most
important member of this network?
Can we conclude this from his
position in the network?
What if Andrew is a janitor who
has the keys to every office and no
power whatsoever!
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(a) Star
Andrew
Bob
Charles
David
Emma
Greg
Fynn
And what the CEO does not need
a key to the office: others open
the door for him!

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• Closeness centrality: An actor is considered important if he is
relatively close to all other actors. Closeness is based on the inverse
of the distance of each actor to every other actor in the network. It
measures how fast information propagates from a node to the others.
Fynn is the member with the highest closeness
centrality, followed by Andrew
Hansen (2002) found that projects
whose members have higher degree
of closeness centrality (i.e., short path
lengths) in their knowledge network
were more likely to be completed
more quickly than those whose
members have lower degree of
closeness centrality
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Measures

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• Betweenness centrality: it measures how central a person
is in a network by examining the fraction of shortest paths
between individual pairs of team members that pass through
that person.
Andrew and Fynn have the higher
betweeness centrality
A person who lies on the path
of others can control the
communication flow, and thus
becomes an important and
influential member of the
network
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Measures
It points out those who act as
communication bottlenecks

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Combined interpretation 36
Low Degree Low Closeness Low Betweeness
High Degree Embededd in a
portion of the
network that is far
from the rest of
the network
The node’s
connections are
redundant and
communication
bypasses the node
itself
High Closeness Key player tied to
important or active
nodes
Probably multiple
paths in the
network, the node
is near many
people, but so are
many others
High
Betweeness
The node’s few ties
are crucial for
network flow (of
information,
exchanges,
collaborations etc.)
Very rare cell: the
node monopolizes
the ties from a
small number of
people to many
others

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Measures
• Brokerage: indicates when an actor, named broker, connects two
otherwise unconnected actors or subgroups. Brokerage occurs
when, in a triad of actors A, B, and C, A has a tie to B, B has a tie to
C, but A has no tie to C. A needs B to reach C, therefore B is a
broker.The actors need to be partitioned into subgroups per
attribute [Gould and Fernandez, 1989].
Fynn brokers information among
his developer colleagues
Hinds and McGrath (2006) found that
brokers effectively disseminate information
between distributed sites when maintaining
direct relationships is not practical
Ehrlich et al. (2008) found that brokers are
usually the most knowledgeable members
of a team regardless of geographical
location
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Marczak et al. (2008) confirm Ehrlich et al.
(2008) findings in a study of multiple
distributed teams of a large IT multinational

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Measures
• Cutpoint: indicates a weak point in the network. If this
actor were removed along with his connections, the network
would become divided into unconnected parts. A set of
cutpoints is called a cutset.
Andrew and Fynn are the cutset
In communication
networks a cutpoint
indicates disruption of
information flow
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Measures
• Network structure
• Network centralization
• Core-periphery
• Ties reciprocity
• Clique
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Measures
• Network centralization: quantifies the difference between the
number of ties for each node divided by the maximum possible sum
of differences.A centralized network (index = 1) structure will have
many of its ties dispersed around one or a few actors while a
decentralized network structure (index = 0) is one in which there
is little variation between the number of ties each actor possesses
[Freeman, 1978].
Centralization index = 0.39
Tsai (2002) found that a
formal hierarchical
structure in the form of
centralization has a
significant negative effect on
knowledge sharing among
organizational units
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Measures
• Core-periphery: indicates the extent to which the structure of a
network consists of two classes of actors: a cohesive subnetwork, the
core, in which the actors are connected to each other in some
maximal sense; and a class of actors that are more loosely connected
to the cohesive subnetwork but lack any maximal cohesion with the
core, the peripheral actors. A high core value (close to 1) indicates a
strong core-periphery structure [Borgatti and Everett, 1999].
Core-periphery index = 0.47
Hinds and McGrath (2006)
found that communication
networks with a strong core-
periphery structure leads to
less coordination problems
than loosely connected
networks
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Measures
• Ties reciprocity: when the relationship is considered directional (e.g.,
friendship, trust), then the reciprocity index can be calculated using the dyad
method, the ration of the number of pairs of actors with a reciprocated ties
relative to the number of pairs with any tie between the actors; or the arc
method, the ration of the number of ties that are involved in reciprocal
relationships relative to the total number of actual ties [Hanneman and Riddle, 2005].
Dyad method index = 0.85
The higher the index of
reciprocal ties the more stable
or equal the network structure
is [Rao and Bandyopadhyay, 1987].
A higher reciprocity index
suggests a more horizontal
structure while the opposite
suggests a more hierarchical
network [Hanneman and Riddle, 2005].
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Measures
• Clique: consists of a subset of at least 3 actors in which
every possible pair of actors is directly connected by a tie
and there are no other actors that are also directly
connected to all members of the clique [Wasserman and Faust, 1994].
- Andrew, Bob, Charles, and David
- Andrew, David, and Emma
- Fynn, Iris, John, and Kevin
Cain et al. (1996) found 3
large cliques consisting of
team members developing 3
major activities: architecture
design, code development,
and code review in the
communication networks of
development teams
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Measures
• Network structure and Evolution
• Triadic closure
• Clustering coefficient
• Structural holes
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• Network structure and Evolution
“What are the mechanisms by which nodes
arrive and depart, and by which ties form
and vanish?”
Measures

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Measures
• Triadic Closure: If two people in a social network have a
friend in common, then there is an increased likelihood that
they will become friends themselves at some point in the
future [Skyrms, 2003] This pattern can be
identified when one
observes the network
behavior for a long time
window
Two developers who do
not know each other who
seek information from
another 3rd
developer are
likely to quickly help each
other when put together to
work when the 3rd
developer is also allocated
to the project
[Easley and Kleinberg, 2010]

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• Reasons for having triadic closure:
• Opportunity: One reason why B and C are more likely to
become friends, when they have a common friend A, is simply
based on the opportunity for B and C to meet
• Trust: the fact that each of B and C is friends with A
(provided they are mutually aware of this) gives them a basis
for trusting each other
• Incentive: if A is friends with B and C, then it becomes a
source of latent stress in these relationships if B and C are not
friends with each other
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Measures
[Easley and Kleinberg, 2010]

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• Structural Holes:
• Span asymmetric information: love triangle, also known as
‘forbidden triad’, brokerage activity of entrepreneurs, bankers, brokers
or real-estate agents
• Bridge entire communities: we will see the local bridges and their
role in connecting graph components and spreading novelty
Structural hole
Advantageous position of B, based on his
position in the network. People as B are
network bridges
Pawlowski and Robey (2004) examine knowledge
brokering as an aspect of the work of
information technology professionals.
Measures
[Tvesovat and Kouznetsov, 2011]

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Data collection
• Manual
• Survey
• Work diary
• Observation
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Data collection 50

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Data collection
• Automatic
• Mining software repositories
• E.g.: source-code, bug trackers
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Coffee Break
We are back in
30 minutes
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> Tools
• Gephi
• UCINet
• NetMiner
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Gephi
https://gephi.org/
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• It is a tool for the interactive visualization and exploration of
networks and graphs

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Graph files: Defining the input file that describes the
network
-Definition of type and idtype
-# of nodes and their IDs
-Definition of ties (weight is optional)
.gexf
<graph defaultedgetype="undirected” idtype="string”>
<nodes count="77”>
<node id="0.0" label="Myriel"/>
<node id="1.0" label="Napoleon"/>
…
<edge id="235" source="72.0" target="27.0"/>
<edge id="237" source="73.0" target="48.0" weight="2.0"/>
…
</edges>
</graph>
</gexf>
Gephi

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Opening a .gexf graph file
.gexf
When the file is opened, the report sums up data found and issues:
• Number of nodes
• Number of edges
• Type of graph

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Opening a .gexf graph file
.gexf
Preliminary overview
of the network
Preliminary overview
of the network

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Layout the graph
• Force-based algorithm: linked nodes attract each-other and vice-versa
Select ‘Force Atlas’

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The goal
• To obtain a meaningful representation of the network (i.e. with respect to
context, available information about nodes and ‘meaning’ and strength of
connections, goals of our study, etc.)
Visual representation of
node degree, cliques, edge
weight as a preliminary step
to a deeper analysis using
SNA metrics

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Layout the graph
• Set the Repulsion strength (eg. to 10000) and Run

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Node ranking (colors)
• Choose a rank parameter (eg. Node degree) and set the colors
• Nodes will be colored according to the color range between yellow (lowest
degree =1) and dark orange (higher degree)

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Edge ranking (colors)
You can do the same with edge weight

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Other options
• You can control the thickness of edges, the visbility and size of node labels
• And manage different dragging modes

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Metrics
• Metrics are available in the right
section of the Gephi interface
• Eg. Click on ‘Run’ here, to calculate
the average path length of the
network

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New node values
• Metrics generates reports but also new information
available for each node.
• Thus, by launching the ‘Average path length’ algorithm, we
now have Betweeness Centrality, Closeness Centrality
and Eccentricity for each node
• Let’s try to rank again nodes according to Betweeness
Centrality

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Node size
• Let’s express the node Betweeness Centrality using node
size
• Colors will remain the indicator of the node Degree
Centrality

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You should now see a colored and sized graph
• Color expresses Degree
• Size expresses Betweeness

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Node labels

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Save and export
.gexf
.gephi.gephi
Input
.gephi.gephi
.pdf.pdf
Output
export
project
project
text

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Opening
a .gexf file
Creating a .gephi
project
Exporting in .pdf

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Opening a .gephi project
.gephi.gephi

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UCINet
https://sites.google.com/site/ucinetsoftware/home
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NetMiner
http://www.netminer.com
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> Hand-on Exercises
• Time to practice and do it yourself!
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Exercise 1
• Let’s explore what the visualization of a
social network can offer us [20 min]
• Enter the dataset made available at the Gephi
tool and brainstorm with others what insights
you can have about the network from its visual
representation
• Save the visualization in a separated file
• Share what you have learned with the
participants
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Exercise 2
• Let’s explore a measure [15 min]
• Calculate the centrality measure (degree,
closeness, and betweenness) for the network
loaded in the Exercise 1
participants
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Exercise 3
• Let’s highlight the results [15 min]
• Choose how to represent the node attributes,
e.g. by coloring the nodes according to the
attributes and/or by showing them role in the
node labels
participants
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Exercise 4
• Exporting the results [5 min]
• Save the results in PDF format
• Save the results again, now as a Gephi project
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> Final Remarks
• What one wants to learn from the social
networks
• Plan ahead: Design to collect proper data
• Use a tool to provide support to
understanding the collected dataset
• Contextual information is necessary for
comprehension of what the tool points out
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> Recommended reading
• Rob Cross and Andrew
Parker.The Hidden
Power of Social
Networks: Understanding
How work Really Gets
Done in Organizations.
Harvard Business School
Press, Boston, United
States, June 2004.
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• John Scott. Social Network
Analysis:A Handbook. Sage
Publications, London,
England, 2nd edition, March
2000.
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• StanleyWasserman and
Katherine Faust. Social
Network Analysis: Methods
and Applications. Cambridge
University Press, Cambridge,
United Kingdom, 1994.
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• Kate Ehrlich and Klarissa Chang. Leveraging Expertise in Global
Software Teams: Going Outside Boundaries. In IEEE Proc. of the
International Conference on Global Software Engineering, 149–
158, Florianópolis, Brazil, October 2006.
• Marcelo Cataldo, Patrick Wagstrom, James Herbsleb, and
Kathleen Carley. Identification of Coordination Requirements:
Implications for the Design of Collaboration and Awareness
Tools. In ACM Proc. of the Conference on Computer Supported
Cooperative Work, 353–362, Ban , Canada, November 2006.ﬀ
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> References
• [Mitchell, 1969] J. Clyde Mitchell. Social Networks in Urban Situations:Analyses of
Personal Relationships in Central African Towns. Manchester University Press,
Manchester, United Kingdom, November 1969.
• [Herbsleb and Mockus, 2003] James Herbsleb and Audris Mockus.An Empirical
Study of Speed and Communication in Globally Distributed Software Development.
IEEE Transactions on Software Engineering, 29(6): 481–494, June 2003.
• [Hinds and McGrath, 2006] Pamela Hinds and Cathleen McGrath. Structures
that Work: Social Structure,Work Structure and Coordination Ease in
Geographically Distributed Teams. In ACM Proc. of the Conference on Computer
Supported Cooperative Work, 343–352, Banff, Canada, November 2006.
• [Damian et al., 2007] Daniela Damian, Sabrina Marczak, and Irwin Kwan.
Collaboration Patterns and the Impact of Distance on Awareness in Requirements-
Centred Social Networks. In IEEE Proc. of the Int’l Requirements Engineering
Conference, 59-68, New Delhi, India, October 2007.
• [Freeman, 1978] Linton Freeman. Centrality in Social Networks: Conceptual
Clarification. Social Networks, 1(3): 215–239, 1978/1979.
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> References
• [Tsai, 2002] Wenpin Tsai. Social Structure of ”Coopetition” Within a Multiunit
Organization: Coordination, Competition, and Intraorganizational Knowledge
Sharing. Organization Science, 13(2):179–190, March 2002.
• [Borgetti and Everett, 1999] Stephen Borgatti and Martin Everett. Models of
Core/Periphery Structures. Social Networks, 21(4): 375–395, October 1999.
• [Hanneman and Riddle, 2005] Robert Hanneman and Mark Riddle.
Introduction to Social Network Methods. University of California, Riverside, United
States, 2005.
• [Rao and Bandyopadhyay, 1987] Ramachandra Rao and Sura Bandyopadhyay.
Measures of Reciprocity in a Social Network. Sankhya:The Indian Journal of
Statistics, Series A, 49(2): 141–188, June 1987.
• [Wasserman and Faust, 1994] Stanley Wasserman and Katherine Faust. Social
Network Analysis: Methods and Applications. Crambidge University Press,
Crambidge, United Kingdom, 1994.
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> References
• [Cain et al., 1996] Brendan Cain, James Coplien, and Neil Harrison. Social
Patterns in Productive Software Development Organizations.Annals of Software
Engineering, 2(1): 259–286, 1996.
• [Freeman et al., 1979] Linton Freeman, Douglas Roeder, and Robert Mulholland.
Centrality in Social Networks: II. Experimental Results. Social Networks, 2(2):119–
141, 1979/1980.
• [Hossain et al., 2006] Liaquat Hossain,Andre Wu, and Kennetg Chung.Actor
Centrality Correlates to Project Based Coordination. In ACM Proc. of the
Conference on Computer Supported Cooperative Work, 363–372, Ban , Canada,ﬀ
November 2006.
• [Bird et al., 2006] Christian Bird,Alex Gourley, Premkumar Devanbu, Michael
Gertz, and Anand Swaminathan. Mining Email Social Networks. In ACM Proc. of the
Int’l Workshop on Mining Software Repositories, 37–143, Shanghai, China, May
2006.
• [Hansen, 2002] Morten Hansen. Knowledge Networks: Explaining Effective
Knowledge Sharing in Multiunit Companies. Organization Science, 13(3):232–248,
June 2002.
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> References
• [Ehrlich et al., 2008] Kate Ehrlich, Mary Helander, GiuseppeValetto, Stephen Davies, and
Clay Williams.An Analysis of Congruence Gaps and Their Effect on Distributed Software
Development. In Workshop on Socio-Technical Congruence, in conj. with the Int’l Conf. on
Software Eng., Leipzig, Germany, May 2008.ACM.
• [RE ‘08] Sabrina Marczak, Daniela Damian, Ulrike Stege, and Adrian Schroeter,
“Information Brokers in Requirements-Dependency Social Networks”, In: IEEE Proc.
International Requirements Engineering Conference, Barcelona, Spain, 53-62, September
2008.
• [Skyrms, 2003] Brian Skyrms.The Stag Hunt and Evolution of Social Structure.
Cambridge University Press, 2003.
• [Easley and Kleinberg, 2010] David Easley and Jon Kleinberg. Networks, Crowds, and
Markets: Reasoning About a Highly Connected World. Cambridge University Press New
York, NY, 2010
• [Tvesovat and Kouznetsov, 2011] Maksim Tvesovat and Alexander Kouznetsov. Social
Network Analysis for Startups – Finding Connections on the Social Web. O’ Reilly, 2011.
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Thank you for you interest!
Questions? Comments? Suggestions?
Sabrina Marczak
PUCRS, Porto Alegre, Brazil
sabrina.marczak@pucrs.br
ICGSE 2013
8th IEEE International Conference on Global Software Engineering
Bari, Italy | August 26-29, 2013
www.icgse.org
Nicole Novielli
Uniba, Bari, Italy
nicole.novielli@uniba.it

Social Network Analysis for Global Software Engineering: Exploring relationships from a fine-grained level @ICGSE 2013

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