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While smart contracts have the potential to revolutionize many important applications like banking, trade, and supply-chain, their reliable deployment begs for rigorous formal verification. Since most smart contracts are not annotated with formal specifications, general verification of functional properties is impeded. In this work, we propose an automated approach to verify unannotated smart contracts against specifications ascribed to a few manually-annotated contracts. In particular, we propose a notion of behavioral refinement, which implies inheritance of functional properties. Furthermore, we propose an automated approach to inductive proof, by synthesizing simulation relations on the states of related contracts. Empirically, we demonstrate that behavioral simulations can be synthesized automatically for several ubiquitous classes like tokens, auctions, and escrow, thus enabling the verification of unannotated contracts against functional specifications.
Blockchain, [INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO], [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], Refinement, Smart contracts, Simulation
Blockchain, [INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO], [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], Refinement, Smart contracts, Simulation
citations 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). | 10 | |
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. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |