  Ethereum has become a widely used platform to enable secure, Blockchain-based
financial and business transactions. However, many identified bugs and
vulnerabilities in smart contracts have led to serious financial losses, which
raises serious concerns about smart contract security. Thus, there is a
significant need to better maintain smart contract code and ensure its high
reliability. In this research: (1) Firstly, we propose an automated deep
learning based approach to learn structural code embeddings of smart contracts
in Solidity, which is useful for clone detection, bug detection and contract
validation on smart contracts. We apply our approach to more than 22K solidity
contracts collected from the Ethereum blockchain, results show that the clone
ratio of solidity code is at around 90%, much higher than traditional software.
We collect a list of 52 known buggy smart contracts belonging to 10 kinds of
common vulnerabilities as our bug database. Our approach can identify more than
1000 clone related bugs based on our bug databases efficiently and accurately.
(2) Secondly, according to developers' feedback, we have implemented the
approach in a web-based tool, named SmartEmbed, to facilitate Solidity
developers for using our approach. Our tool can assist Solidity developers to
efficiently identify repetitive smart contracts in the existing Ethereum
blockchain, as well as checking their contract against a known set of bugs,
which can help to improve the users' confidence in the reliability of the
contract. We optimize the implementations of SmartEmbed which is sufficient in
supporting developers in real-time for practical uses. The Ethereum ecosystem
as well as the individual Solidity developer can both benefit from our
research.
