Source Code for Paper: Extracting Smart Contracts Tested and Verified in Coq

Annenkov, Danil; Milo, Mikkel; Nielsen, Jakob Botsch; Spitters, Bas

doi:10.1145/3410271

Cited by 1 publication

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“…Our development is available as an accompanying artifact [AMNS20] and in the GitHub repository https://github.com/AU-COBRA/ConCert/tree/artifact-2020.…”

Section: Cic λ□mentioning

confidence: 99%

Extracting smart contracts tested and verified in Coq

Annenkov

Milo

Nielsen

et al. 2021

Proceedings of the 10th ACM SIGPLAN International Conference on Certified Programs and Proofs

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We implement extraction of Coq programs to functional languages based on MetaCoq's certified erasure. As part of this, we implement an optimisation pass removing unused arguments. We prove the pass correct wrt. a conventional call-by-value operational semantics of functional languages. We apply this to two functional smart contract languages, Liquidity and Midlang, and to the functional language Elm. Our development is done in the context of the ConCert framework that enables smart contract verification. We contribute a verified boardroom voting smart contract featuring maximum voter privacy such that each vote is kept private except under collusion of all other parties. We also integrate property-based testing into ConCert using QuickChick and our development is the first to support testing properties of interacting smart contracts. We test several complex contracts such as a DAO-like contract, an escrow contract, an implementation of a Decentralized Finance (DeFi) contract which includes a custom token standard (Tezos FA2), and more. In total, this gives us a way to write dependent programs in Coq, test them semi-automatically, verify, and then extract to functional smart contract languages, while retaining a small trusted computing base of only MetaCoq and the pretty-printers into these languages.

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“…Our development is available as an accompanying artifact [AMNS20] and in the GitHub repository https://github.com/AU-COBRA/ConCert/tree/artifact-2020.…”

Section: Cic λ□mentioning

confidence: 99%