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SUMMARY:Automated Error Diagnosis Using Abductive Inference - Thomas Dilli
 g\, College of William and Mary\, Virginia
DTSTART:20130207T100000Z
DTEND:20130207T110000Z
UID:TALK43425@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:When program verification tools fail to verify a program\, ei
 ther the program is buggy or the report is a false alarm. In this situatio
 n\, the burden is on the user to manually classify the report\, but this t
 ask is time-consuming\, error-prone\, and does not utilize facts already p
 roven by the analysis. We present a new technique for assisting users in c
 lassifying error reports. Our technique computes small\, relevant queries 
 presented to a user that capture exactly the information the analysis is m
 issing to either discharge or validate the error.  Our insight is that ide
 ntifying these missing facts is an instance of the abductive inference pro
 blem in logic\, and we present a new algorithm for computing the smallest 
 and most general abductions in this setting. We perform the first user st
 udy to rigorously evaluate the accuracy and effort involved in manual clas
 sification of error reports.  Our study demonstrates that our new techniq
 ue is very useful for improving both the speed and accuracy of error repor
 t classification. 
LOCATION:Small Lecture Theatre\, Microsoft Research Ltd\, 21 Station Road\
 , Cambridge\, CB1 2FB
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