# Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
> 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.
- Authors: Yunlong Feng; Dechuan Teng; Yang Xu; Honglin Mu; Xiao Xu; Libo Qin; Qingfu Zhu; Wanxiang Che
- 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
- Sources reviewed: 2026-10-18
- Added to the catalog: 2026-10-19
- Original paper:
- PDF:
- Code:
- BibTeX:
- Catalog page:
- Record JSON:
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-sc2dec/index.md)
## 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 notes
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%. At the 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.
## Primary sources
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## About this entry
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
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