Structure and Chance: Melding Logic and Probability for Software Debugging

Lisa Burnell

Computer Science Department
University of Texas
Arlington, Texas

Eric Horvitz

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 programming error.

Keywords: Software maintenance, decision theory, automated diagnosis, probability, Bayesian reasoning

In: Communications of the ACM, 38:3, pages 31-41, 1995.