SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
📰 ArXiv cs.AI
Learn how to evaluate LLM-based smart contract decompilers using SCDBench, a novel benchmark for assessing decompiler performance and semantic consistency
Action Steps
- Build a dataset of smart contracts with known source code and bytecode
- Run SCDBench to evaluate the performance of an LLM-based decompiler
- Configure the benchmark to use relevant metrics and semantic consistency checks
- Test the decompiler on the benchmark dataset
- Compare the results with other decompilers to identify areas for improvement
Who Needs to Know This
Developers and researchers working on smart contract decompilation and LLM-based tools can benefit from SCDBench to evaluate and improve their decompilers
Key Insight
💡 SCDBench provides a comprehensive framework for evaluating the performance and semantic consistency of LLM-based smart contract decompilers
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🚀 Introducing SCDBench: a benchmark for evaluating LLM-based smart contract decompilers 📊
Key Takeaways
Learn how to evaluate LLM-based smart contract decompilers using SCDBench, a novel benchmark for assessing decompiler performance and semantic consistency
Full Article
Title: SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
Abstract:
arXiv:2605.29059v1 Announce Type: cross Abstract: Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We intro
Abstract:
arXiv:2605.29059v1 Announce Type: cross Abstract: Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We intro
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