sVerify: Verifying Smart Contracts Through Lazy Annotation and Learning

[1]  Nikhil Swamy,et al.  Formal Verification of Smart Contracts: Short Paper , 2016, PLAS@CCS.

[2]  Valentin Wüstholz,et al.  Harvey: a greybox fuzzer for smart contracts , 2019, ESEC/SIGSOFT FSE.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[5]  Prateek Saxena,et al.  Finding The Greedy, Prodigal, and Suicidal Contracts at Scale , 2018, ACSAC.

[6]  A data-driven CHC solver , 2018, PLDI.

[7]  Ákos Hajdu,et al.  solc-verify: A Modular Verifier for Solidity Smart Contracts , 2019, VSTTE.

[8]  Prateek Saxena,et al.  Making Smart Contracts Smarter , 2016, IACR Cryptol. ePrint Arch..

[9]  Sukrit Kalra,et al.  ZEUS: Analyzing Safety of Smart Contracts , 2018, NDSS.

[10]  Bo Gao,et al.  sCompile: Critical Path Identification and Analysis for Smart Contracts , 2018, ICFEM.

[11]  Isil Dillig,et al.  Formal Verification of Workflow Policies for Smart Contracts in Azure Blockchain , 2019, VSTTE.

[12]  Ye Liu,et al.  ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).

[13]  Petar Tsankov,et al.  Securify: Practical Security Analysis of Smart Contracts , 2018, CCS.

[14]  Helmut Veith,et al.  Counterexample-guided abstraction refinement for symbolic model checking , 2003, JACM.

[15]  Kenneth L. McMillan Lazy Annotation for Program Testing and Verification , 2010, CAV.

[16]  Stuart Haber,et al.  How to Time-Stamp a Digital Document , 1990, CRYPTO.