# DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts

arXiv: https://arxiv.org/abs/2403.16861 · HTML: https://mlsanity.com/paper/2403.16861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Do you still need a manual smart contract audit?](https://mlsanity.com/paper/2306.12338/) (2023)
- [How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection](https://mlsanity.com/paper/2005.11613/) (2020)
- [Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding](https://mlsanity.com/paper/2309.09826/) (2023)
- [DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects](https://mlsanity.com/paper/2305.08456/) (2023)
- [Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?](https://mlsanity.com/paper/2304.02981/) (2023)
- [Automated Summarization of Stack Overflow Posts](https://mlsanity.com/paper/2305.16680/) (2023)
- [sGUARD: Towards Fixing Vulnerable Smart Contracts Automatically](https://mlsanity.com/paper/2101.01917/) (2021)
- [Too Few Bug Reports? Exploring Data Augmentation for Improved Changeset-based Bug Localization](https://mlsanity.com/paper/2305.16430/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair](https://mlsanity.com/paper/2503.01098/) (2025)

## Abstract (arXiv)

The DISL dataset features a collection of 514,506 unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts. By aggregating every verified smart contract from Etherscan up to January 15, 2024, DISL surpasses existing datasets in size and recency.