Empirical Review of Automated Analysis Tools on 47 587 Ethereum Smart Contracts

Proceedings of the 42nd International Conference on Software Engineering, Main track (ICSE'20)

T. DurieuxJ. F. FerreiraR. AbreuP. Cruz 

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Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.

author = {Thomas Durieux and
Jo{\~{a}}o F. Ferreira and
Rui Abreu and
Pedro Cruz},
editor = {Gregg Rothermel and
Doo{-}Hwan Bae},
title = {Empirical review of automated analysis tools on 47, 587 Ethereum smart
booktitle = {{ICSE} '20: 42nd International Conference on Software Engineering,
Seoul, South Korea, 27 June - 19 July, 2020},
pages = {530--541},
publisher = {{ACM}},
year = {2020},
url = { https://doi.org/10.1145/3377811.3380364 },
doi = {10.1145/3377811.3380364},
timestamp = {Tue, 13 Apr 2021 13:32:35 +0200},
biburl = { https://dblp.org/rec/conf/icse/DurieuxFAC20.bib },
bibsource = {dblp computer science bibliography,  https://dblp.org }
Last Updated: 28/07/2021