Learning universal probabilistic models for fault localization

Recently there has been significant interest in employing probabilistic techniques for fault localization. Using dynamic dependence information for multiple passing runs, learning techniques are used to construct a probabilistic graph model for a given program. Then, given a failing run, the probabilistic model is used to rank the executed statements according to the likelihood of them being faulty. In this paper we present a novel probabilistic approach in which universal probabilistic models are learned to characterize the behaviors of various instruction types used by all programs. The universal probabilistic model for an instruction type is in form of a probability distribution that represents how errors in the input (operand) values are propagated as errors in the output (result) of a given instruction type. Once these models have been constructed, they can be used in the analysis of any program as follows. Given a set of runs for any program, including at least one passing and ...

• Type: Conference
• Year: 2010
• Where: PASTE
• Authors: Min Feng, Rajiv Gupta
• Keywords: Instruction Type, PASTE 2010, Probabilistic Models, Universal Probabilistic Models