Analysing the practice of distributed software engineers: combining social network analysis and interaction analysis

Pål Fugelli, Faculty of Education

Analysing the practice of distributed software engineers: combining social network analysis and interaction analysis

Prøveforelesning/trial lecture 10.09-2010

Pål Fugelli, Faculty of Education 10.09.2010

Lecture overviewGeographically distributed software engineering

• What is it about?• Time and place matrix• Examples

Social Network Analysis (SNA)• Network data• Boundaries• Ties• A range of formal methods• Density, centrality and cliques • Example research study

Interaction Analysis (IA)• Underlying assumptions• The use of video• Framed by ethnographic fieldwork• Observing distributed teams

A combined approach• Network framing• Implications/final remarks


Software engineering

• Addresses all aspects of the software development process (Daintith & Wright, 2008).

• Increasingly a team-based activity (Elleithy, 2010).• Technical aspects, but also a social process (Dittrich,

Randall, & Singer, 2009).• Requires co-ordination and communication.


Time and place matrix adapted from Johansen (1988)

Same place(co-located interaction)

Different place(distributed interaction)

Same time(Synchronous communication)

Local work context. Face-to-face interaction.

Remote interactions. Video conferencing.Shared view desktop conferencing systems .

Different time(Asynchronous communication)

Continous task. I.e. team rooms, local project management.

Communication and coordination.E-mail correspondence. Version control.


Examples of geographically distributed software development projects

• Microsoft Windows operative system (Bird et al., 2009).

• Open source projects (see e.g. Lanzara, 2005).


Social Network Analysis (SNA)

• A theoretical perspective and research tools examining social structures.

• The study of social relations among a set of actors.• The unit of analysis is an entity consisting of a

collection of individuals and the linkages among them.

• Tabular form referred to as Adjacency matrix. Contain as many rows and columns as there are actors in the data set.

(Wasserman & Faust, 1994)


Basic network data (n=4)

A1: Bill, A2: Steve, A3:Linus, A4: Edith


Population boundaries

Full network analysis Ego-centric network


Network ties

• Defining what ties or relations to be measured.

• Online Interactions/communication patterns.


A range of formal methods to represent social networks

• Mathematics and graphs.• Computer assisted analysis.

• i.e. a combination of Ucinet and NetDraw (www.Analytictech.com) .

• Recommended measures: • Density• Centrality • Cliques


Density

• Density measures express the general level of cohesion in the social network (Scott, 2000).

• Defined by Garton, et al., (1999) as “the number of actually occurring relations or ties as the proportion of the number of theoretically possible relations of ties (p. 84).


Degree centrality

The number of other points that have a direct relation to that node. This is the sum of each row in the adjacency matrix representing the network (Freeman, 1979).


Cliques and sub-groups

• A clique is a maximal complete sub-graph of three or more nodes (Wasserman & Faust, 1994).

• Sub-sets of actors who are more closely tied to each other (Hanneman, 2005).


Example overlapping clique sets dev-1dev-2

dev-3

dev-4dev-5dev-6dev-7dev-8dev-9dev-10dev-11

dev-12

dev-13dev-14dev-15dev-16dev-17dev-18dev-19

dev-20

dev-21

dev-22

dev-23

dev-24dev-25dev-26dev-27dev-28

dev-29dev-30dev-31dev-32dev-33dev-34dev-35dev-36dev-37

dev-38

dev-39

dev-40

dev-41dev-42dev-43dev-44dev-45dev-46dev-47dev-48dev-49dev-50dev-51dev-52dev-53dev-54dev-55dev-56dev-57dev-58dev-59dev-60dev-61dev-62dev-63dev-64

dev-65

dev-66dev-67dev-68dev-69dev-70dev-71dev-72dev-73dev-74dev-75dev-76dev-77dev-78dev-79dev-80

dev-81

dev-82dev-83dev-84dev-85dev-86dev-87dev-88dev-89dev-90dev-91

dev-92

dev-93dev-94dev-95dev-96dev-97dev-98dev-99dev-100dev-101dev-102dev-103dev-104dev-105dev-106dev-107dev-108dev-109dev-110dev-111dev-112dev-113dev-114dev-115dev-116dev-117dev-118dev-119dev-120dev-121dev-122dev-123dev-124dev-125dev-126dev-127dev-128dev-129dev-130dev-131dev-132dev-133dev-134dev-135dev-136dev-137dev-138dev-139dev-140dev-141dev-142dev-143

dev-144

dev-145dev-146dev-147dev-148dev-149dev-150dev-151dev-152dev-153dev-154dev-155dev-156dev-157dev-158dev-159dev-160dev-161dev-162dev-163dev-164dev-165dev-166dev-167dev-168dev-169dev-170dev-171dev-172dev-173dev-174dev-175dev-176dev-177dev-178dev-179dev-180dev-181dev-182dev-183dev-184dev-185dev-186dev-187dev-188dev-189dev-190dev-191dev-192dev-193dev-194dev-195dev-196dev-197dev-198dev-199dev-200dev-201dev-202dev-203dev-204dev-205dev-206dev-207dev-208dev-209dev-210dev-211dev-212dev-213dev-214dev-215

1

2

3

4


Example research study

Communication networks in geographically distributed software development (Cataldo & Herbsleb, 2008).

RQ1: Does a highly interconnected group of people take on a disproportionate share of overall communication?


Communication patterns evolving over time


Limitations of the study

”It is also worth pointing out that we did not have the opportunity to observe all communication, for example face-to face, telephone, and video conference.”

(Cataldo & Herbsleb, 2008:587)


Interaction analysis (IA)

IA is an interdisciplinary method for empirical investigation of the interaction of human beings with each other and with objects of their environment. It investigates human activities, such as talk, nonverbal interaction, and the use of artefacts and technologies, identifying routine practices and problems and the resources for their solution.

(Jordan and Henderson, 1995)


Underlying assumptions

I. Expert knowledge and practices are situated in the interactions between members of a particular community that are engaged with object and artefacts in their environment.

II. Finds the empirical data in the details of social interactions extended in time and space.


The use of video

• “Video technology has been vital in establishing Interaction Analysis” (Jordan & Henderson, 1995:1).

• Creates relatively permanent primary records.• Group work analysis; Collaborative viewing of

selected sequences of interaction. • In-situ video recordings to reconstruct events.


Framed by ethnographic fieldwork

• Video-based Interaction Analysis in conjunction with ethnographic fieldwork is quite common.

• “In the course of this ethnographic work, we attempt to identify interactional ‘hot spots’ -- sites of activity for which videotaping promises to be productive” (Jordan and Henderson, 1995:3).


Distributed teams of software engineers

Work environments spanning multiple physical locations.

CSCW system

Potential data sources: - Personal interviews- Electronic activity logs - Video observations- Reference books- Whiteboards - Desktop applications - Web applications


Observing distributed teams

• Capture interactions between team members, artifacts and objects at the different physical locations.

• Observing distributed team meetings such as video conferencing, screen logging and activity logs generated by CSCW-platform.

• Retrospective analysis; merging data from distributed sites in order to reconstruct complex interactions (Ruhleder, 2000).


A combined approach?

• As analysts, to move from social interactions to their sum we need an instrument (Latour, 1996).

• Social network incorporated with video-based IA.• Top down: Descriptive level, organize data to

prepare for video-based IA. Providing an overview of the sum of interacting dyads in a communication network.


Network framing

• SNA as a framing for selecting ‘interactional hotspots’?

• Bottom up: account for distributed network relations when conducting micro analysis of social interaction.


Implications, pros and cons

- The network stand leads us towards a relational perspective on social structure.

- Multisite video data. Micro level studies of moment-to-moment social interaction at the different physical sites.

- Combined in a retrospective analysis; sync or merge the activities across multiple locations.

- Complex, resource intensive design?- May differ from the ”true” structure of the network

(Wasserman & Faust, 1994).


References

Documents

Analysing the practice of distributed software engineers: combining social network analysis and interaction analysis