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Counterexample Driven Refinement for Abstract Interpretation
[chapter]
2006
Lecture Notes in Computer Science
interpretation techniques prove properties of programs by computing abstract fixpoints. All such analyses suffer from the possibility of false errors. We present a new counterexample driven refinement technique to reduce false errors in abstract interpretations. Our technique keeps track of the precision losses during forward fixpoint computation, and does a precise backward propagation from the error to either confirm the error as a true error, or identify a refinement so as to avoid the false
doi:10.1007/11691372_34
fatcat:so77twiywjbsnikaugufr43v34