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• W2004936005 abstract "In this paper, we explore the application of counter-example-guided abstraction refinement (CEGAR) in the context of microprocessor correspondence checking. The approach utilizes automatic datapath abstraction augmented with automatic refinement based on 1) localization, 2) generalization, and 3) minimal unsatisfiable subset (MUS) extraction. We introduce several refinement strategies and empirically evaluate their effectiveness on a set of microprocessor benchmarks. The data suggest that localization, generalization, and MUS extraction from both the abstract and concrete models are essential for effective verification. Additionally, refinement tends to converge faster when multiple MUses are extracted in each iteration." @default.
• W2004936005 created "2016-06-24" @default.
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• W2004936005 date "2006-01-01" @default.
• W2004936005 modified "2023-09-26" @default.
• W2004936005 title "Refinement strategies for verification methods based on datapath abstraction" @default.
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• W2004936005 doi "https://doi.org/10.1145/1118299.1118306" @default.
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