Model-based diagnosis

an intelligent diagnosis theory has been successfully applied in software fault localization with promising results.However

traditional MBD relies on the assumption that components in the system fail dependently which makes the diagnoses with high false positives in software fault localization.In this paper

a component redundancy analysis approach is presented.The approach not only uses Craig interpolant to filter redundant components

but also employs a fact that a branch predicate evaluates to either true or false to filter some branch condition components.Experimental results show that the proposed approach effectively reduces the false positive rates of diagnoses

i.e.

reducing the number of diagnosis by 48.4%

and reducing the number of nodes of hitting set tree generated during diagnosis computation by 47.6%.