SolEval: Benchmarking Large Language Models for Repository-level Solidity Code Generation

• URL: http://arxiv.org/abs/2502.18793v1
• Date: Wed, 26 Feb 2025 03:58:31 GMT
• Title: SolEval: Benchmarking Large Language Models for Repository-level Solidity Code Generation
• Authors: Zhiyuan Peng, Xin Yin, Rui Qian, Peiqin Lin, Yongkang Liu, Chenhao Ying, Yuan Luo,
• Abstract summary: We construct SolEval, the first repository-level benchmark for Solidity smart contract generation.<n>Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity.<n>Results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement.
• Score: 20.36430282456073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts. Due to the lack of adequate benchmarks for Solidity, LLMs' ability to generate secure, cost-effective smart contracts remains unexplored. To fill this gap, we construct SolEval, the first repository-level benchmark designed for Solidity smart contract generation, to evaluate the performance of LLMs on Solidity. SolEval consists of 1,125 samples from 9 different repositories, covering 6 popular domains, providing LLMs with a comprehensive evaluation benchmark. Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity of the Ethereum ecosystem by incorporating gas fee and vulnerability rate. We evaluate 10 LLMs on SolEval, and our results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement in Solidity code generation by LLMs. We release our data and code at https://anonymous.4open.science/r/SolEval-1C06/.

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