An Introduction to Social Network Analysis and Its Application in Software Engineering
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This is a short tutorial on social network analysis applied to software engineering for beginners. Main social network analysis are presented along with examples of their application from literature. Reading recommendation is provided. This material was presented at the Workshop on Agile Methods for Distributed Teams organized by Prof. Tayana Conte, UFAM, Manaus, Brazil, on late Nov 2012.
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An Introduction to Social Network Analysis and Its Application in Software Engineering
- 1. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Manaus, Novembro 2012 An Introduction to Social Network Analysis and its application in Software Engineering Sabrina Marczak sabrina.marczak@pucrs.br
- 2. Software Development 2 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 3. Software Development 3 Conception Planning R. Analyst P. Manager Design Development Architect Developer Testing Deployment Tester Developer Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 4. Software Development 3 Conception Planning R. Analyst P. Manager R. Analyst Design Development Requirement Architect Developer Tester Architect Testing Deployment Tester Developer Developer P. Manager Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 5. Software Development 4 Goals Tasks • Collaboration Depen • Coordination dencies • Communication Deadlines Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 6. Software Development 5 • Who talks with whom? • Who receives help from whom? • Who is aware of whom? • Who are the experts? • Who are the most active contributors? Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 7. Software Development 6 • 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? Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 8. Software Development 7 • How to answer to these questions? Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 9. Software Development 7 • How to answer to these questions? Social Network Analysis Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 10. Social network analysis 8 • It provides techniques to examine the structure of social relationships in a group to uncover patterns of behavior and interaction among people [Mitchell, 1969] Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 11. Agenda 9 • Introduction to social network analysis • My research on collaboration using SNA • Recommended reading Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 12. > Introduction to SNA 10 • Terminology • Representation • Measures • Data collection • Tools Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 13. Terminology 11 Actor Charles Bob Greg Hannah Fynn David Andrew Iris Emma Kevin John Lucas Actor = Node = Vertice Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 14. Terminology 12 Bob Charles Greg Hannah Tie Fynn David Andrew Iris Emma Kevin John Lucas Tie = Link = Edge Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 15. Terminology 13 Dyad Charles Bob Greg Hannah Fynn David Andrew Iris Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 16. Terminology 14 Bob Charles Greg Hannah Triad Fynn David Andrew Iris Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 17. Representation 15 • Sociogram Bob Charles Greg Hannah Fynn David Andrew Iris Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 18. Representation 16 • Matrix representation of network data Absent Present Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 19. Representation 17 • Actors’ attributes Role Country Work exp. Andrew 1 1 3 Role Bob 1 2 3 1. Tester 2. Developer Charles 1 1 1 David 1 2 2 Country Emma 1 1 1 1. Canada 2. Ireland Fynn 2 1 3 Greg 2 1 1 Work experience 1. 1-6 months Hannah 2 1 1 2. 6-12 months Iris 2 1 2 3.18+ months John 2 2 3 Kevin 2 2 2 Lucas 2 1 2 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 20. Representation 18 • Sociogram with actors’ attributes Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 21. Representation 18 • Sociogram with actors’ attributes Bob Charles Greg Hannah Legend Developer Tester Canada Fynn Ireland 1-6 months 6-12 months David Iris Andrew 18+ months Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 22. Representation 19 • Tie weight • Strength • Frequency • Etc... Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 23. Measures 20 • Overall network characterization • Network size • Network density • Ties statistics Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 24. Measures 21 • Network size: is the number of actors in the social network Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 25. Measures 21 • Network size: is the number of actors in the social network Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Size: 12 actors Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 26. Measures 21 • Network size: is the number of actors in the social network Bob Size can be larger or smaller than the team size Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Size: 12 actors Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 27. Measures 21 • Network size: is the number of actors in the social network Bob Size can be larger or smaller than the team size Charles Greg Hannah Fynn David Iris Andrew Emma Herbsleb and Mockus (2003) Kevin found that distributed Lucas John communication networks are significantly smaller than same-site networks Size: 12 actors Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 28. Measures 22 • 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. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 29. Measures 22 • 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. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Possible ties: 12 (12-1) / 2 = 66 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 30. Measures 22 • 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. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Possible ties: 12 (12-1) / 2 = 66 Density: 20 / 66 = 0.30 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 31. Measures 22 • 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. Bob Charles Greg Hinds and McGrath (2006) found Hannah that geographic distribution is Fynn associated with less dense work David Andrew Iris ties and less dense information Emma sharing, suggesting that social ties Kevin are not particularly important in John distributed as compared with Lucas collocated teams as a means of coordinating work and improving Possible ties: 12 (12-1) / 2 = 66 performance Density: 20 / 66 = 0.30 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 32. Measures 23 • Ties statistics: it uses the actors’ attributes to reveal overall network characteristics Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 33. Measures 23 • Ties statistics: it uses the actors’ attributes to reveal overall network characteristics Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas E.g.: 5 testers and 7 developers Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 34. Measures 23 • Ties statistics: it uses the actors’ attributes to reveal overall network characteristics Bob By counting up the number Charles Greg Hannah of ties within and cross-sites, Herbesleb and Mockus Fynn (2003) found that there is David Andrew Iris much more frequent Emma communication with local Kevin colleagues in a distributed John project Lucas E.g.: 5 testers and 7 developers Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 35. Measures 24 • Network structure • Network centralization • Core-periphery • Ties reciprocity • Clique Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 36. Measures 25 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 37. Measures 25 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Centralization index = 0.39 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 38. Measures 25 • 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]. Bob Charles Greg Hannah Tsai (2002) found that a formal hierarchical Fynn structure in the form of David Andrew Iris centralization has a Emma significant negative effect Kevin on knowledge sharing among organizational units John Lucas Centralization index = 0.39 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 39. Measures 26 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 40. Measures 26 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Core-periphery index = 0.47 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 41. Measures 26 • 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]. Bob Charles Greg Hannah Hinds and McGrath (2006) found that communication Fynn networks with a strong core- David Andrew Iris periphery structure leads to Emma less coordination problems Kevin than loosely connected John networks Lucas Core-periphery index = 0.47 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 42. Measures 27 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 43. Measures 27 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Dyad method index = 0.85 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 44. Measures 27 • 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]. Bob Charles Greg Hannah The higher the index of reciprocal ties the more stable Fynn or equal the network structure David is [Rao and Bandyopadhyay, 1987]. Iris Andrew Emma Kevin John Lucas Dyad method index = 0.85 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 45. Measures 27 • 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]. Bob Charles Greg Hannah The higher the index of reciprocal ties the more stable Fynn or equal the network structure David is [Rao and Bandyopadhyay, 1987]. Iris Andrew Emma A higher reciprocity index Kevin suggests a more horizontal John structure while the opposite Lucas suggests a more hierarchical Dyad method index = 0.85 network [Hanneman and Riddle, 2005]. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 46. Measures 28 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 47. Measures 28 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas - Andrew, Bob, Charles, and David - Andrew, David, and Emma - Fynn, Iris, John, and Kevin - Fynn, John, Kevin, and Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 48. Measures 28 • 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]. Bob Charles Greg Hannah Cain and colleagues (1996) Fynn found 3 large cliques David Andrew Iris consisting of team members Emma developing 3 major activities: Kevin architecture design, code John development, and code Lucas review in the communication networks of a certain - Andrew, Bob, Charles, and David - Andrew, David, and Emma development team - Fynn, Iris, John, and Kevin - Fynn, John, Kevin, and Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 49. Measures 29 • Information exchange • Reachability • Component • Degree centrality Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 50. Measures 30 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 51. Measures 30 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas All actors are reachable Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 52. Measures 30 • 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]. Bob Charles Greg Hannah If some actors cannot reach others, there is a Fynn potential division in the network and thus David Iris Andrew Emma information cannot reach everyone Kevin John Lucas All actors are reachable Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 53. Measures 31 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 54. Measures 31 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas One component Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 55. Measures 31 • 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]. Bob Charles Greg Hannah Component test indicates whether there is a group of Fynn people connected to each David Andrew Iris other and disconnected from Emma the remaining, while clique Kevin test indicates whether a John subset of actors is Lucas completely connected One component Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 56. Measures 32 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 57. Measures 32 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Fynn is the member with the highest out- and in-degree Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 58. Measures 32 • 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]. Charles Bob Hossain and colleagues (2006) found Greg Hannah that highly centralized members coordinate better than others Fynn David Iris Andrew Emma Kevin John Lucas Fynn is the member with the highest out- and in-degree Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 59. Measures 32 • 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]. Charles Bob Hossain and colleagues (2006) found Greg Hannah that highly centralized members coordinate better than others Fynn David Iris Bird and colleagues (2006) found Andrew that degree centrality indicated Emma that developers who actually Kevin committed changes played much John more significant roles in the email Lucas community than non-developers Fynn is the member with the highest out- and in-degree Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 60. Measures 33 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 61. Measures 33 • 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]. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Fynn brokers information among his developer colleagues Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 62. Measures 33 • 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]. Bob Hinds and McGrath (2006) found Charles Greg Hannah that brokers effectively disseminate information between distributed Fynn sites when maintaining direct David relationships is not practical Iris Andrew Emma Kevin John Lucas Fynn brokers information among his developer colleagues Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 63. Measures 33 • 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]. Bob Hinds and McGrath (2006) found Charles Greg Hannah that brokers effectively disseminate information between distributed Fynn sites when maintaining direct David relationships is not practical Iris Andrew Emma Ehrlich and colleagues (2008) Kevin found that brokers are usually the John most knowledgeable members of Lucas a team regardless of geographical Fynn brokers information among location his developer colleagues Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 64. Measures 34 • 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. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 65. Measures 34 • 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. Bob Charles Greg Hannah Fynn David Iris Andrew Emma Kevin John Lucas Andrew and Fynn are the cutset Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 66. Measures 34 • 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. Bob Charles Greg Hannah In communication networks a cutpoint Fynn indicates disruption of David Iris Andrew Emma information flow Kevin John Lucas Andrew and Fynn are the cutset Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 67. Data collection 35 • Manual • Survey • Work diary • Observation Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 68. Data collection 36 Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 69. Data collection 37 • Automatic • Mining software repositories • E.g.: source-code, bug trackers Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 70. Tools 38 • UCINet https://sites.google.com/site/ucinetsoftware/home Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 71. Tools 39 • NetMiner http://www.netminer.com Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 72. Tools 40 • Gephi https://gephi.org/ Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 73. > My research 41 • RE ’07: Patterns • RE ’08: Brokerage • Book Ch. ’10: RDC framework • RE ’11: Roles and communication • ICSE ’12: Domain knowledge Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 74. > My research 42 • RE ’07: Collaboration patterns and impact of distance on awareness Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 75. > My research 43 • RE ’08: Brokerage Brokerage predominant in certain types of communication Distance didn’t matter Knowledge and experience as determinants for brokerage Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 76. > My research 44 • Book ch. ’10: RDC framework Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 77. > My research 45 • RE ’11: Roles and communication structures Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 78. > My research 46 • ICSE ’13: Domain knowledge and hierarchical control structures in coordination Communication ties that do not follow task assignments but are according to hierarchical structure Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 79. > Recommended reading 47 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. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 80. > Recommended reading 48 John Scott. Social Network Analysis: A Handbook. Sage Publications, London, England, 2nd edition, March 2000. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 81. > Recommended reading 49 Stanley Wasserman and Katherine Faust. Social Network Analysis: Methods and Applications. Crambidge University Press, Crambidge, United Kingdom, 1994. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 82. > Recommended reading 50 • 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. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 83. > References 51 [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. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 84. > References 52 [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. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 85. > References 53 [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. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 86. > References 54 [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). Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Sabrina Marczak - Manaus, Novembro 2012
- 87. Workshop de Métodos Ágeis para Desenvolvimento Distribuído de Software Manaus, Novembro 2012 Thank you for your attention! Questions? Sabrina Marczak sabrina.marczak@pucrs.br http://www.inf.pucrs.br/sabrina.marczak/