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Functional Analysis of Large Software Networks

Functional Analysis of Large Software Networks

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Functional Analysis of Large Software Networks

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  1. Functional Analysis of Large Software Networks Natasa Przulj, Gordon Lee and Igor Jurisica IBM CAS, University of Toronto

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

  3. Software Call Graphs PSQL 7.3 Call Graph (R. Holt, J. Wu)

  4. 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

  5. Properties of Large Networks: • Degree distribution • Diameter • Clustering Degree distrib. ex. (NSW, Phys Rev E, 64):

  6. 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

  7. Graph Properties

  8. 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

  9. Lines of Code (LOC)

  10. LOC Statistics for graph groups for PSQL 7.3:

  11. 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

  12. 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)