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2025 · Conference paper

Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation

Hanzhuo Tan; Xiaolong Tian; Hanrui Qi; Jiaming Liu; Zuchen Gao; Siyi Wang; Qi Luo; Jing Li; Yuqun Zhang

Introduces two million C/C++ binary-source function pairs using a Compile-Trace-Filter pipeline, with an evaluation suite spanning translated HumanEval/MBPP tasks and recent GitHub functions.

Scope and limitations

This is function-level decompilation. Re-execution tests cover HumanEval/MBPP; GitHub2025 provides no unit tests. Inlining can prevent one-to-one source correspondence, and the fine-tuning study uses 10% of the corpus. These results do not establish whole-program recovery.

Why it fits this collection

Adds a training corpus and evaluation artifact for LLM decompilation, distinct from Gao et al.’s existing DecompileBench entry.

Publication and review details

Venue
NeurIPS 2025 Datasets and Benchmarks
Status
Conference paper
Publication / posting date
2025 (exact date not verified)
Date source
Publication year in the official NeurIPS proceedings and publisher-deposited Crossref metadata; no finer publication date is asserted.
First arXiv submission
May 19, 2025
arXiv
2505.12668
Reviewed arXiv version
v2
DOI
10.52202/085713-0177

Author order follows the final PDF, which puts Zuchen Gao before Siyi Wang; the proceedings landing page and Crossref reverse them. arXiv v2 was revised on 2025-10-19. The artifact is the decompile-bench subdirectory of the existing LLM4Decompile repository. Code has an MIT license; the dataset declares CC0-1.0, while model weights have separate terms. These declarations do not replace source-project license review.

Sources reviewed

Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.

Primary sources

  1. https://papers.nips.cc/paper_files/paper/2025/hash/079cf13ae174c31f148207d94d213bdc-Abstract-Datasets_and_Benchmarks_Track.html
  2. https://papers.nips.cc/paper_files/paper/2025/file/079cf13ae174c31f148207d94d213bdc-Paper-Datasets_and_Benchmarks_Track.pdf
  3. https://doi.org/10.52202/085713-0177
  4. https://api.crossref.org/works/10.52202/085713-0177
  5. https://arxiv.org/abs/2505.12668
  6. https://arxiv.org/html/2505.12668v2
  7. https://github.com/albertan017/LLM4Decompile/blob/85b364bf093eb2ee4f3687cfe38a203fca89f23e/decompile-bench/readme.md
  8. https://github.com/albertan017/LLM4Decompile/blob/85b364bf093eb2ee4f3687cfe38a203fca89f23e/LICENSE
  9. https://github.com/albertan017/LLM4Decompile/blob/85b364bf093eb2ee4f3687cfe38a203fca89f23e/LICENSE-MODEL
  10. https://huggingface.co/datasets/LLM4Binary/decompile-bench/blob/4b708c2211cd7d4af403675db56322aa4ed7050c/README.md
  11. https://huggingface.co/datasets/LLM4Binary/decompile-bench/blob/4b708c2211cd7d4af403675db56322aa4ed7050c/LICENSE