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di Angelo, M., & Salzer, G. (2020). Characterizing Types of Smart Contracts in the Ethereum Landscape. In Financial Cryptography and Data Security (pp. 389–404). Springer. https://doi.org/10.1007/978-3-030-54455-3_28
After cryptocurrencies, smart contracts are the second major innovation of the blockchain era. Leveraging the immutability and accountability of blockchains, these event-driven programs form the basis of the new digital economy with tokens, wallets, exchanges, and markets, but facilitating also new models of peer-to-peer organizations. To judge the long-term prospects of particular projects and th...
After cryptocurrencies, smart contracts are the second major innovation of the blockchain era. Leveraging the immutability and accountability of blockchains, these event-driven programs form the basis of the new digital economy with tokens, wallets, exchanges, and markets, but facilitating also new models of peer-to-peer organizations. To judge the long-term prospects of particular projects and this new technology in general, it is important to understand how smart contracts are used. While public announcements, by their nature, make promises of what smart contracts might achieve, openly available data of blockchains provides a more balanced view on what is actually going on.
We focus on Ethereum as the major platform for smart contracts and aim at a comprehensive picture of the smart contract landscape regarding common or heavily used types of contracts. To this end, we unravel the publicly available data of the main chain up to block 9000000, in order to obtain an understanding of almost 20 million deployed smart contracts and 1.5 billion interactions. As smart contracts act behind the scenes, their activities are only fully accessible by also considering the execution traces triggered by transactions. They serve as the basis for this analysis, in which we group contracts according to common characteristics, observe temporal aspects and characterize them quantitatively and qualitatively. We use static methods by analyzing the bytecode of contracts as well as dynamic methods by aggregating and classifying the communication between contracts.
