USING SOCIAL NETWORK
ANALYSIS FOR SOFTWARE
ICEMI 2014, HONG KONG 15-16 FEBRUARY 2014
PROF. PANOS FITSILIS
TECHNOLOGICAL EDUCATION INSTITUTE OF THESSALY
• 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
• Integration, scope, time, cost, quality, HR,
• IPMA Competence Baseline
• Technical, behavioral, contextual
• Agile methods
• XP, Scrum, Crystal Reports, etc.
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.
Using Social Networking to discover the
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?
SOCIAL NETWORKS AND KNOWLEDGE MANAGEMENT
• Why Social Networks in KMS?
Knowledge Management involves people, technology, and processes in
TRANSFORMING TACIT KNOWLEDGE TO EXPLICIT
Software Project Ontology
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
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
• 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)