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https://doi.org/10.1109/cdc.20...
Article . 2006 . Peer-reviewed
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Model Checking For Fault Explanation

Authors: Shengbing Jiang; Thomas E. Fuhrman; Sumit Kumar Jha 0001;

Model Checking For Fault Explanation

Abstract

Model checking is very effective at finding out even subtle faults in system designs. A counterexample is usually generated by model checking algorithms when a system does not satisfy the given specification. However, a counterexample is not always helpful in explaining and isolating faults in a system when the counterexample is very long, which is usually the case for large scale systems. As such, there is a pressing need to develop fault explanation and isolation techniques. In this paper, we present a new approach for the fault explanation and isolation in discrete event systems with LTL (linear-time temporal logic) specifications. The notion of fault seed is introduced to characterize the cause of a fault. The identification of the fault seed is further reduced to a model checking problem. An algorithm is obtained for the fault seed identification. An example is provided to demonstrate the effectiveness of the approach developed.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average