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2024 · Conference paper

Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement

Yunlong Feng; Dechuan Teng; Yang Xu; Honglin Mu; Xiao Xu; Libo Qin; Qingfu Zhu; Wanxiang Che

Adds two techniques to LLM decompilation of function-level x86-64 GCC assembly into C. Self-Constructed Context Decompilation (sc²dec) recompiles a model’s first compilable output and uses the resulting assembly–source pair as an in-context example for a second attempt, without fine-tuning. Fine-grained Alignment Enhancement (FAE) fine-tunes llm4decompile-6.7b with LoRA on statement-level assembly–C alignments derived from DWARF debug information. Combining both, the authors report 52.41% average re-executability on Decompile-Eval, against 48.60% for the base model.

Scope and limitations

Results are function-level re-executability on Decompile-Eval: 164 HumanEval-derived C problems compiled at O0–O3 and checked by recompilation and HumanEval test cases. Passing these tests is not whole-program or all-input equivalence, and the paper does not evaluate real-world binaries, other architectures or readability. sc²dec helps only when the first output recompiles, so low re-compilability limits its gains; the authors report 1–2% lower results when context is built at a mismatched optimization level, and clang-built contexts scored slightly below GCC. FAE used 10,000 ExeBench functions limited to the standard C library, one training epoch and a single 6.7B base model; the authors did not validate it on larger datasets or models, and it slightly lowered the 1-shot result. GPT-4 and DeepSeek baselines are API snapshots that may not be reproducible.

Why it fits this collection

Adds recompilation feedback and debug-information alignment to end-to-end LLM decompilation, complementing LLM4Decompile and execution-checked decompilation benchmarks.

Publication and review details

Venue
Findings of EMNLP 2024
Status
Conference paper
Publication / posting date
November 2024
Date source
The official ACL Anthology citation gives November 2024. Crossref records only the year; the November 12–16 conference dates printed in the PDF are not treated as a publication day.
First arXiv submission
June 25, 2024
arXiv
2406.17233
Reviewed arXiv version
v2
DOI
10.18653/v1/2024.findings-emnlp.385

The title keeps the published spelling ‘Fined-grained’. The eight-author order follows the Anthology, PDF, Crossref and arXiv. arXiv v2 (2024-10-03) is labeled EMNLP 2024 Findings; v1 was submitted on 2024-06-25. The Anthology and Crossref give pages 6603–6614. The abstract’s ‘approximately 3.90%’ gain differs from the 3.81-point gap between Table 1’s 48.60% base result and 52.41%; this entry cites the table values. At the reviewed 2025-02-18 commit, the repository declares MIT, although LICENSE.txt keeps template placeholders for year and holders. It provides an evaluation script and GCC Decompile-Eval data, and links a LoRA adapter for LLM4Binary/llm4decompile-6.7b-v1.5 and a training dataset on Hugging Face, both tagged MIT; the base model and ExeBench keep their own terms. Its README results table adds readability scores and the authors’ later ReF Decompile, and its FAE-only averages differ slightly from the paper (51.07% versus 50.88%). The evaluator uses local gcc and objdump and, with --compiler, sends code to the public Compiler Explorer API. No weights or datasets were downloaded, and no code or experiments were run.

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://aclanthology.org/2024.findings-emnlp.385/
  2. https://aclanthology.org/2024.findings-emnlp.385.pdf
  3. https://aclanthology.org/2024.findings-emnlp.385.bib
  4. https://api.crossref.org/works/10.18653/v1/2024.findings-emnlp.385
  5. https://arxiv.org/abs/2406.17233
  6. README.md GitHub · AlongWY/sccdec · Revision 22745ea
  7. LICENSE.txt GitHub · AlongWY/sccdec · Revision 22745ea
  8. pyproject.toml GitHub · AlongWY/sccdec · Revision 22745ea
  9. src/sccdec/eval.py GitHub · AlongWY/sccdec · Revision 22745ea
  10. https://huggingface.co/ylfeng/sccdec-lora
  11. https://huggingface.co/datasets/ylfeng/sccdec-dataset