@Article{jqc.2020.010815,
AUTHOR = {Zaoyu Wei, Jiaqi Wang, Xueqi Shen, Qun Luo},
TITLE = {Smart Contract Fuzzing Based on Taint Analysis and Genetic  Algorithms},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {11--24},
URL = {http://www.techscience.com/jqc/v2n1/39235},
ISSN = {2579-0145},
ABSTRACT = {Smart contract has greatly improved the services and capabilities of 
blockchain, but it has become the weakest link of blockchain security because of its code 
nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure 
the security of blockchain system. Oriented to Ethereum smart contract, the study solves 
the problems of redundant input and low coverage in the smart contract fuzz. In this 
paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a 
dangerous operation database is designed to identify the dangerous input, and genetic 
algorithm is used to optimize the code coverage of the input, which construct the fuzzing 
framework for smart contract together. Finally, by comparing Oyente and ContractFuzzer, 
the performance and efficiency of the framework are proved.},
DOI = {10.32604/jqc.2020.010815}
}