@inproceedings{feng-etal-2024-self,
title = "Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement",
author = "Feng, Yunlong and
Teng, Dechuan and
Xu, Yang and
Mu, Honglin and
Xu, Xiao and
Qin, Libo and
Zhu, Qingfu and
Che, Wanxiang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.385/",
doi = "10.18653/v1/2024.findings-emnlp.385",
pages = "6603--6614",
abstract = "Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. Based on the characteristics of the decompilation task, we propose two methods: (1) Without fine-tuning, the Self-Constructed Context Decompilation (sc$^2$dec) method recompiles the LLM`s decompilation results to construct pairs for in-context learning, helping the model improve decompilation performance. (2) Fine-grained Alignment Enhancement (FAE), which meticulously aligns assembly code with source code at the statement level by leveraging debugging information, is employed during the fine-tuning phase to achieve further improvements in decompilation. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 3.90{\%} on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41{\%}. The code, data, and models are available at https://github.com/AlongWY/sccdec."
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<abstract>Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. Based on the characteristics of the decompilation task, we propose two methods: (1) Without fine-tuning, the Self-Constructed Context Decompilation (sc²dec) method recompiles the LLM‘s decompilation results to construct pairs for in-context learning, helping the model improve decompilation performance. (2) Fine-grained Alignment Enhancement (FAE), which meticulously aligns assembly code with source code at the statement level by leveraging debugging information, is employed during the fine-tuning phase to achieve further improvements in decompilation. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%. The code, data, and models are available at https://github.com/AlongWY/sccdec.</abstract>
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%0 Conference Proceedings
%T Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
%A Feng, Yunlong
%A Teng, Dechuan
%A Xu, Yang
%A Mu, Honglin
%A Xu, Xiao
%A Qin, Libo
%A Zhu, Qingfu
%A Che, Wanxiang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F feng-etal-2024-self
%X Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. Based on the characteristics of the decompilation task, we propose two methods: (1) Without fine-tuning, the Self-Constructed Context Decompilation (sc²dec) method recompiles the LLM‘s decompilation results to construct pairs for in-context learning, helping the model improve decompilation performance. (2) Fine-grained Alignment Enhancement (FAE), which meticulously aligns assembly code with source code at the statement level by leveraging debugging information, is employed during the fine-tuning phase to achieve further improvements in decompilation. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%. The code, data, and models are available at https://github.com/AlongWY/sccdec.
%R 10.18653/v1/2024.findings-emnlp.385
%U https://aclanthology.org/2024.findings-emnlp.385/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.385
%P 6603-6614
Markdown (Informal)
[Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement](https://aclanthology.org/2024.findings-emnlp.385/) (Feng et al., Findings 2024)
ACL
- Yunlong Feng, Dechuan Teng, Yang Xu, Honglin Mu, Xiao Xu, Libo Qin, Qingfu Zhu, and Wanxiang Che. 2024. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6603–6614, Miami, Florida, USA. Association for Computational Linguistics.