Structure and Chance: Melding Logic and Probability for Software Debugging
Computer Science Department
University of Texas
Decision Theory & Adaptive Systems Group
Microsoft Research, 9S
Redmond, Washington 98052
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To date, software engineers charged with debugging complex software
packages have had few automated reasoning tools to assist them with
identifying the sources of error and with prioritizing their effort.
We describe methods, based on a synthesis of logical and probabilistic
reasoning, that can be employed to identify the likely source and
location of problems in complex software. The methods have been
applied to diagnosing run-time errors in the Sabre system, the largest
timeshared reservation system in the world. The results from our
validation suggest that such methods can be of value in directing the
attention of software engineers to program execution paths and program
instructions that have the highest likelihood of harboring a
Keywords: Software maintenance, decision theory, automated diagnosis, probability, Bayesian reasoning
In: Communications of the ACM, 38:3, pages 31-41, 1995.