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Statistical Debugging: Simultaneous Identification of Multiple Bugs Alice X. Zheng alicez@cs.cmu.edu
 

Summary: Statistical Debugging: Simultaneous Identification of Multiple Bugs
Alice X. Zheng alicez@cs.cmu.edu
Carnegie Mellon University, School of Computer Science, Pittsburgh, PA
Michael I. Jordan jordan@cs.berkeley.edu
University of California, Berkeley, Department of EECS, Department of Statistics, Berkeley, CA
Ben Liblit liblit@cs.wisc.edu
Computer Sciences Department, University of Wisconsin-Madison, Madison, WI
Mayur Naik mhn@cs.stanford.edu
Computer Science Department, Stanford University, Stanford, CA
Alex Aiken aiken@cs.stanford.edu
Computer Science Department, Stanford University, Stanford, CA
Abstract
We describe a statistical approach to soft-
ware debugging in the presence of multiple
bugs. Due to sparse sampling issues and com-
plex interaction between program predicates,
many generic off-the-shelf algorithms fail to
select useful bug predictors. Taking inspira-
tion from bi-clustering algorithms, we pro-
pose an iterative collective voting scheme for

  

Source: Aiken, Alex - Department of Computer Science, Stanford University
Jordan, Michael I. - Department of Electrical Engineering and Computer Sciences, University of California at Berkeley

 

Collections: Computer Technologies and Information Sciences