  Smart contracts are programs that execute transactions involving independent
parties and cryptocurrencies. As programs, smart contracts are susceptible to a
wide range of errors and vulnerabilities. Such vulnerabilities can result in
significant losses. Furthermore, by design, smart contract transactions are
irreversible. This creates a need for methods to ensure the correctness and
security of contracts pre-deployment. Recently there has been substantial
research into such methods. The sheer volume of this research makes
articulating state-of-the-art a substantial undertaking. To address this
challenge, we present a systematic review of the literature. A key feature of
our presentation is to factor out the relationship between vulnerabilities and
methods through properties. Specifically, we enumerate and classify smart
contract vulnerabilities and methods by the properties they address. The
methods considered include static analysis as well as dynamic analysis methods
and machine learning algorithms that analyze smart contracts before deployment.
Several patterns about the strengths of different methods emerge through this
classification process.

Cryptography and Security;Software Engineering