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SUMMARY:Probabilistic Graph Models for Debugging Software - Laura Dietz (M
 ax Planck Institute for Computer Science\, Saarbrücken)
DTSTART:20090119T140000Z
DTEND:20090119T150000Z
UID:TALK15958@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Of all software development activities\, debugging---locating 
 the defective source code statements that cause a failure---can be by far 
 the most time-consuming. We employ probabilistic modeling to support progr
 ammers in finding defective code. Most defects are identifiable in control
  flow graphs of software traces. A trace is represented by a sequence of c
 ode positions (line numbers in source filenames) that are executed when th
 e software runs. The control flow graph represents the finite state machin
 e of the program\, in which states depict code positions and arcs indicate
  valid follow up code positions. In this work\, we extend this definition 
 towards an n-gram control flow graph\, where a state represents a fragment
  of subsequent code positions\, also referred to as an n-gram of code posi
 tions. We devise a probabilistic model for such graphs in order to infer c
 ode positions in which anomalous program behavior can be observed. This mo
 del is evaluated on real world data obtained from the open source AspectJ 
 project and compared to the well known multinomial and multi-variate Berno
 ulli model.
LOCATION:Engineering Department\, CBL Room 438
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