DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917622073786368 |
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| author | Morello, Gabriele Eshghie, Mojtaba Bobadilla, Sofia Monperrus, Martin |
| author_facet | Morello, Gabriele Eshghie, Mojtaba Bobadilla, Sofia Monperrus, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts Morello, Gabriele Eshghie, Mojtaba Bobadilla, Sofia Monperrus, Martin Software Engineering Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts |
| topic | Software Engineering Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2403.16861 |