Improving smart contract search by semantic and structural clustering for source codes

Abstract

The search for smart contract source codes has drawn research attention to fulfill developers’ and researchers’ needs. Yet, the existing studies are not mature enough to address smart contracts’ technical properties and functionalities. This paper proposes a system to improve the naive search for smart contract codes; for example, Etherscan has one keyword search feature without regard to the contract structure. We consider clustering smart contracts based on developers’ preferences, which increases the probability that the resulting source codes match developers’ needs. Our experimental results show a significant improvement in the complexity of the retrieved source codes of smart contracts compared with the baseline scenario using blockchain search engines (e.g., Etherscan). Our solution reduces the number of retrieved smart contract codes the developer has to check if the codes match her/his needs by 94%, 88%, 82%, or 98%, depending on the user’s search preferences.