Functional Analysis of Large Software Networks

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Functional Analysis of Large Software Networks

• Natasa Przulj, Gordon Lee and Igor Jurisica
• IBM CAS, University of Toronto

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Overview

• Software Call Graphs
• Models of Large Networks
• Properties of a Software Network (PSQL)
  Preliminary Results
• Bugs versus Network Properties
• Future Work

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Software Call Graphs

• PSQL 7.3 Call Graph (R. Holt, J. Wu)

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Models of Large Networks

• Random Graphs (Erdos, Renyi)
• Generalized Random Graphs (Bander, Canfield)
• Small-World (Watts, Strogatz Newman, Watts)
• Scale-Free (Simon Barabasi, Albert, Jeong)

Strogatz, Nature, 410
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Properties of Large Networks

• Degree distribution
• Diameter
• Clustering

Degree distrib. ex. (NSW, Phys Rev E, 64)
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Degree Distribution
Shortest Path Length Distribution
Nodes of PSQL 7.0, 7.1, 7.2, 7.3 4639, 5127,
5568, 5996
Edges of PSQL 7.0, 7.1, 7.2, 7.3 17010, 19790,
21616, 23802
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Graph Properties
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Bug Reports

• PSQL no proper database of bug reports
  (severity) - limitation
• Used Google Web APIs to search (Ben Vitale)
• http//archives.postgresql.org/,
  http//developer.postgresql.org/

With Bug Reports
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Lines of Code (LOC)
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LOC Statistics for graph groups for PSQL 7.3
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Future Work

• Properties of SW call graphs to aid identifying
  buggy modules
• SW Design versus Network Structure
• Overlap of network clusters with natural
  modules
• CAS interested in similar analysis on DB2

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Thanks

• Igor Jurisica (supervisor, U of Toronto)
• Derek Corneil (supervisor, U of Toronto)
• Gordon Lee (IBM CAS)
• Ric Holt (U of Waterloo)
• Grad students Jingwey Wu, Benjamin Vitale, Wayne
  Hayes, Daniela Rosu, Cristiana Chitic, Travis
  Gagie, Robert Vracaric, Nina Przulj
• IBM Center for Advanced Studies (CAS)