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Functional Analysis of Large Software Networks Natasa Przulj, Gordon Lee and Igor Jurisica IBM CAS,...
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Functional Analysis of Large Functional Analysis of Large Software NetworksSoftware Networks
Natasa Przulj, Gordon Lee and Igor JurisicaIBM CAS, University of Toronto
Overview:Overview:
Software Call GraphsModels of Large NetworksProperties of a Software Network (PSQL):
Preliminary ResultsBugs versus Network PropertiesFuture Work
Models of Large NetworksModels 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
Properties of Large Networks:Properties of Large Networks: Degree distribution Diameter Clustering
Degree distrib. ex. (NSW, Phys Rev E, 64):
# 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 Distribution Shortest Path Length Distribution
Bug ReportsBug 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
Future WorkFuture Work
Properties of SW call graphs to aid identifying buggy modules
SW Design versus Network StructureOverlap of network clusters with “natural
modules”CAS interested in similar analysis on DB2
Thanks: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)