# Characterizing Code Clones in the Ethereum Smart Contract Ecosystem

arXiv: https://arxiv.org/abs/1905.00272 · HTML: https://mlsanity.com/paper/1905.00272/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 7 of 7)

- [SourceP: Detecting Ponzi Schemes on Ethereum with Source Code](https://mlsanity.com/paper/2306.01665/) (2023)
- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions](https://mlsanity.com/paper/2604.27763/) (2026)
- [Detecting DeFi Securities Violations from Token Smart Contract Code](https://mlsanity.com/paper/2112.02731/) (2021)
- [Towards Understanding Deep Learning Model in Image Recognition via Coverage Test](https://mlsanity.com/paper/2505.08814/) (2025)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)
- [Esim: EVM Bytecode Similarity Detection Based on Stable-Semantic Graph](https://mlsanity.com/paper/2511.12971/) (2025)

## Abstract (arXiv)

In this paper, we present the first large-scale and systematic study to characterize the code reuse practice in the Ethereum smart contract ecosystem. We first performed a detailed similarity comparison study on a dataset of 10 million contracts we had harvested, and then we further conducted a qualitative analysis to characterize the diversity of the ecosystem, understand the correlation between code reuse and vulnerabilities, and detect the plagiarist DApps. Our analysis revealed that over 96% of the contracts had duplicates, while a large number of them were similar, which suggests that the ecosystem is highly homogeneous. Our results also suggested that roughly 9.7% of the similar contract pairs have exactly the same vulnerabilities, which we assume were introduced by code clones. In addition, we identified 41 DApps clusters, involving 73 plagiarized DApps which had caused huge financial loss to the original creators, accounting for 1/3 of the original market volume.