SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
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 introduce SCDBench, a dataset and benchmark methodology for LLM-based smart contract decompilation. The dataset contains 600 real-world Solidity contracts with paired bytecode inputs, ground-truth source code, and replayable semantic checkpoints. SCDBe...
| Authors | Kaihua Qin, Dawn Song, Arthur Gervais |
|---|---|
| Primary category | cs.SE |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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