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. # 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 Degree DistributionShortest Path Length Distribution

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:

12. 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

13. 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)