project knowledge management based on social networks

USING SOCIAL NETWORK ANALYSIS FOR SOFTWARE PROJECT MANAGEMENT

ICEMI 2014, HONG KONG 15-16 FEBRUARY 2014

PROF. PANOS FITSILIS

(FITSILIS@TEILAR.GR)

TECHNOLOGICAL EDUCATION INSTITUTE OF THESSALY

CONTENTS

• Contemporary Trends on Project managements

• How SNA can be used in the context of Software Engineering and Software Project Management

• The ONSOCIAL project

TYPICAL PROJECT MANAGEMENT APPROACHES

• Project Management Institute – Body of Knowledge

• www.pmi.org

• Integration, scope, time, cost, quality, HR, communication,

• PRINCE

• www.prince2.com

• IPMA Competence Baseline

• www.ipma.ch

• Technical, behavioral, contextual

• Agile methods

• XP, Scrum, Crystal Reports, etc.

Process

People

WHAT ARE THE INTANGIBLES IN SPM?

DEFINITION OF INTANGIBLES

The factors not shown in the traditional project analysis,

but which are of critical importance for the project and the organization’s future success.

How we select our team?How we decide on our team composition?

What knowledge we are missing?What requirements to include in our release?

Which tests to execute?Using Social Networking to discover the intangibles

SOCIAL NETWORKS AND KNOWLEDGE MANAGEMENT

• Why Social Networks in KMS?

Knowledge Management involves people, technology, and processes inOverlapping parts.

Knowledge Management

People

ContentProcesses

TRANSFORMING TACIT KNOWLEDGE TO EXPLICIT KNOWLEDGE

TACIT

KNOWLEDGE

EXPLICIT

KNOWLEDGE

SOCIAL NETWORK ANALYSIS

Potential for knowledge extraction

…

Software Project Ontology

Personnel information

Personnel knowledge evaluation

Knowledge management Team selection

Social network

5

SOCIAL NETWORK ANALYSIS

Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities.

The nodes in the network are the people and groups while the links show relationships or flows between the nodes.

We measure Social Network in terms of:

• 1. Degree Centrality:

• The number of direct connections a node has.

• 2. Betweenness Centrality:

• A node with high betweenness has great influence over what flows in the network indicating important links and single point of failure.

• 3. Closeness Centrality:

• The measure of closeness of a node which are close to everyone else.

DIMENSION FOR TEAM SELECTION

Model

Projects

Locations

Knowledge

AgentsTasks

Resources

Roles

NETWORK OF CONNECTIONS/1

NETWORK OF CONNECTIONS/2

COLLABORATION NET FOR AGENTS(AGENT X AGENT)

Project A

Project B

Project C

NETWORK THAT RELATES TEAM MEMBERS, PROJECTS AND KNOWLEDGE

Project team members

Knowledge/skills

Projects

PROJECT NETWORK PRESENTING CLASSES KNOWLEDGE AND PROJECTS

Knowledge/skills

Projects

Knowledge required by each project

ONSOCIAL PROJECT TECHNICAL ARCHITECTURE

PROBLEMS NEED TO BE ADDRESSED

• Data extraction from different Social Networks

• Sparse data usage (profiles are empty)

• Unique identification of profiles

• Creating/keeping current enterprise data corpus

• Selecting most appropriate algorithm for matching profiles.

CONCLUSION

• We have presented

• How social network analysis can be used in order to improve software project management

• Different scenarios that can help improve project analysis

• Project selection

• Team and knowledge analysis

• Requirements management

• Improving testing

• Based on solid theoretical framework (graph theory)