An Introduction to Social Network Analysis and Its Application in Software Engineering

An Introduction toSocial Network Analysisand its application in Software Engineering

Sabrina [email protected]

Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Manaus, Novembro 2012

Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012

Software Development2


Software Development

R. Analyst

Architect

P. Manager

Developer

DeveloperTester

Planning

Design Development

Testing Deployment

Conception

3



R. Analyst

Architect

P. Manager

Developer

DeveloperTester

Planning

Design Development

Testing Deployment

Conception

R. Analyst

P. Manager

Architect

Developer

Tester

Requirement

3



• Collaboration

• Coordination

• Communication

GoalsTasks

Dependencies

Deadlines

4



• Who talks with whom?

• Who receives help from whom?

• Who is aware of whom?

• Who are the experts?

• Who are the most active contributors?

5



• 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?

6



• How to answer to these questions?

7



• How to answer to these questions?Social Network Analysis

7


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]

8


Agenda

• Introduction to social network analysis

• My research on collaboration using SNA

• Recommended reading

9


> Introduction to SNA

• Terminology

• Representation

• Measures

• Data collection

• Tools

10


Terminology

Actor

Actor = Node = Vertice

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

11


Terminology

Tie

Tie = Link = Edge

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

12


TerminologyDyad

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

13


Terminology

Triad

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

14


Representation

• Sociogram

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

15


Representation

• Matrix representation of network data

Absent

Present

16


Representation

• Actors’ attributesRole 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

Role1. Tester2. Developer

Country1. Canada2. Ireland

Work experience1. 1-6 months2. 6-12 months3.18+ months

17


Representation

• Sociogram with actors’ attributes

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

18


Representation

• Sociogram with actors’ attributes

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Developer

Tester

Canada

Ireland

1-6 months

6-12 months

18+ months

Legend

18


Representation

• Tie weight

• Strength

• Frequency

• Etc...

19


Measures

• Overall network characterization

• Network size

• Network density

• Ties statistics

20


Measures

• Network size: is the number of actors in the social network

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

21


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Size: 12 actors

21


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Size: 12 actors

Size can be larger or smaller than the team size

21


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

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

21


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.

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

22




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Possible ties: 12 (12-1) / 2 = 66

22




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Possible ties: 12 (12-1) / 2 = 66

Density: 20 / 66 = 0.30

22




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

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

22


Measures• Ties statistics: it uses the actors’

attributes to reveal overall network characteristics

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

23




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

E.g.: 5 testers and 7 developers

23




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

E.g.: 5 testers and 7 developers

By counting up the number of ties within and cross-sites,

Herbesleb and Mockus (2003) found that there is

much more frequent communication with local colleagues in a distributed

project

23


Measures

• Network structure

• Network centralization

• Core-periphery

• Ties reciprocity

• Clique

24


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

25




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Centralization index = 0.39

25




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

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

25


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

26




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Core-periphery index = 0.47

26




Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

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

26


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

27


Measures


Dyad method index = 0.85

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

27


Measures



The higher the index of reciprocal ties the more stable or equal the network structure

is [Rao and Bandyopadhyay, 1987]. Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

27


Measures



The higher the index of reciprocal ties the more stable or equal the network structure

is [Rao and Bandyopadhyay, 1987]. Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

A higher reciprocity index suggests a more horizontal

structure while the opposite suggests a more hierarchical network [Hanneman and Riddle, 2005].

27


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

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

28


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

- Andrew, Bob, Charles, and David- Andrew, David, and Emma- Fynn, Iris, John, and Kevin- Fynn, John, Kevin, and Lucas

28


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

- Andrew, Bob, Charles, and David- Andrew, David, and Emma- Fynn, Iris, John, and Kevin- Fynn, John, Kevin, and Lucas

Cain and colleagues (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 a certain

development team

28


Measures

• Information exchange

• Reachability

• Component

• Degree centrality

29


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

30


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

All actors are reachable

30


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

All actors are reachable

If some actors cannot reach others, there is a potential division in the

network and thus information cannot reach

everyone

30


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

31


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

One component

31


Measures


Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

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

31


Measures

• Degree centrality: indicates the number of ties of an actor and is indicative of activity. 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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

32


Measures


Fynn is the member with the highest out- and in-degree

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

32


Measures



Hossain and colleagues (2006) found that highly centralized members coordinate better than others

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

32


Measures



Hossain and colleagues (2006) found that highly centralized members coordinate better than others

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Bird and colleagues (2006) found that degree centrality indicated that developers who actually

committed changes played much more significant roles in the email community than non-developers

32


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].

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

33




Fynn brokers information among his developer colleagues

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

33





Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Hinds and McGrath (2006) found that brokers effectively disseminate

information between distributed sites when maintaining direct relationships is not practical

33





Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

Hinds and McGrath (2006) found that brokers effectively disseminate

information between distributed sites when maintaining direct relationships is not practical

Ehrlich and colleagues (2008) found that brokers are usually the most knowledgeable members of a team regardless of geographical

location

33


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

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

34


Measures


Andrew and Fynn are the cutset

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

34


Measures


Andrew and Fynn are the cutset

Andrew

Bob

Charles

David

Emma

Fynn

Greg Hannah

Iris

John

Kevin

Lucas

In communication networks a cutpoint

indicates disruption of information flow

34


Data collection

• Manual

• Survey

• Work diary

• Observation

35


Data collection36


Data collection

• Automatic

• Mining software repositories

• E.g.: source-code, bug trackers

37


Tools

• UCINet

https://sites.google.com/site/ucinetsoftware/home

38


Tools

• NetMiner

http://www.netminer.com

39


Tools

• Gephi

https://gephi.org/

40


> My research

• RE ’07: Patterns

• RE ’08: Brokerage

• Book Ch. ’10: RDC framework

• RE ’11: Roles and communication

• ICSE ’12: Domain knowledge

41


> My research

• RE ’07: Collaboration patterns and impact of distance on awareness

42


> My research

• RE ’08: Brokerage

Brokerage predominant in certain types of communication

Distance didn’t matter

Knowledge and experience as determinants for brokerage

43


> My research

• Book ch. ’10: RDC framework

44


> My research

• RE ’11: Roles and communication structures

45


> My research

• ICSE ’13: Domain knowledge and hierarchical control structures in coordination

Communication ties that do not follow task assignments but are according to hierarchical structure

46


> 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.

47



John Scott. Social Network Analysis: A Handbook. Sage Publications, London, England, 2nd edition, March 2000.

48



Stanley Wasserman and Katherine Faust. Social Network Analysis: Methods and Applications. Crambidge University Press, Crambidge, United Kingdom, 1994.

49



• 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, Banff, Canada, November 2006.

50


> 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.

[Freeman, 1978] Linton Freeman. Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3): 215–239, 1978/1979.

[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.

51


> References

[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.

[Cain and colleagues, 1996] Brendan Cain, James Coplien, and Neil Harrison. Social Patterns in Productive Software Development Organizations. Annals of Software Engineering, 2(1): 259–286, 1996.

52


> References

[Freeman and colleagues, 1979] Linton Freeman, Douglas Roeder, and Robert Mulholland. Centrality in Social Networks: II. Experimental Results. Social Networks, 2(2):119–141, 1979/1980.

[Hossain and colleagues, 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, Banff, Canada, November 2006.

[Bird and colleagues, 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.

[Ehrlich and colleagues, 2008] Kate Ehrlich, Mary Helander, Giuseppe Valetto, Stephen Davies, and Clay Williams. An Analysis of Congruence Gaps and Their Effct on Distributed Software Development. In Workshop on Socio-Technical Congruence, in conj. with the Int’l Conference on Software Engineering, Leipzig, Germany, May 2008. ACM.

53


> References[RE ‘07] Daniela Damian, Sabrina Marczak, and Irwin Kwan, “Collaboration Patterns and the Impact of Distance on Awareness in Requirements-Centred Social Networks”, In: IEEE Proc. International Requirements Engineering Conference, New Delhi, India, 59-68, 2007.

[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.

[Book ch. ‘10] Daniela Damian, Irwin Kwan, and Sabrina Marczak, Requirements-Driven Collaboration: Leveraging the Invisible Relationships between Requirements and People, Collaborative Software Engineering, Mistrik, I., Grundy, J., van der Hoek, A, Whitehead, J. (Eds.), Chapter 3, pages 57-76, Springer-Verlag, London, England, March 2010.

[RE ‘11] Sabrina Marczak and Daniela Damian, “How Interaction Between Roles Shapes the Communication Structure in Requirements-Driven Collaboration”, In: IEEE Proc. International Requirements Engineering Conference, Trento, Italy, 47-56, 2011.

[ICSE ’13] Daniela Damian, Remko Helms, Irwin Kwan, Sabrina Marczak, and Benjamin Koelewijn, “The Role of Domain Knowledge and Hierarchical Control Structures in Socio-Technical Coordination”, In: IEEE International Conference on Software Engineering, San Francisco, USA, May 2013 (To appear).

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Sabrina [email protected]://www.inf.pucrs.br/sabrina.marczak/

Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Manaus, Novembro 2012

Thank you for your attention!

Questions?

http://www.inf.pucrs.br/sabrina.marczak/

http://www.inf.pucrs.br/sabrina.marczak/

An Introduction to Social Network Analysis and Its Application in Software Engineering

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