A Data Extraction Methodology for Ethereum Smart Contracts
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Barbara Re · 2024
The broader adoption of blockchain for creating decentralised applications has raised interest in employing analysis techniques to support continuous improvement. Data extraction is crucial in this context, as it permits a better understanding of how applications behave. However, due to the variety of data sources (e.g., transactions and events) and the characterisation of the blockchain structure, several challenges arise in automatically extracting data. In particular, retrieving smart contract state changes remains unexplored despite its potential usage for discovering unexpected behaviour. For such reasons, this work proposes a methodology and a supporting tool for extracting data from smart contract executions and state changes. The obtained data is then offered in a way that can be easily converted to purpose-specific standards. The methodology was tested on the PancakeSwap Ethereum bridge smart contract.