@inproceedings{gao-etal-2025-decompilebench,
title = "{D}ecompile{B}ench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios",
author = "Gao, Zeyu and
Cui, Yuxin and
Wang, Hao and
Qin, Siliang and
Wang, Yuanda and
Bolun, Zhang and
Zhang, Chao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1194/",
doi = "10.18653/v1/2025.findings-acl.1194",
pages = "23250--23267",
ISBN = "979-8-89176-256-5",
abstract = "Decompilers are fundamental tools for critical security tasks, from vulnerability discovery to malware analysis, yet their evaluation remains fragmented. Existing approaches primarily focus on syntactic correctness through synthetic micro-benchmarks or subjective human ratings, failing to address real-world requirements for semantic fidelity and analyst usability. We present **DecompileBench**, the first comprehensive framework that enables effective evaluation of decompilers in reverse engineering workflows through three key components: real-world function extraction (comprising 23,400 functions from 130 real-world programs), runtime-aware validation, and automated human-centric assessment using LLM-as-Judge to quantify the effectiveness of decompilers in reverse engineering workflows. Through a systematic comparison between six industrial-strength decompilers and six recent LLM-powered approaches, we demonstrate that LLM-based methods surpass commercial tools in code understandability despite 52.2{\%} lower functionality correctness. These findings highlight the potential of LLM-based approaches to transform human-centric reverse engineering. We open source **DecompileBench** to provide a framework to advance research on decompilers and assist security experts in making informed tool selections based on their specific requirements."
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<abstract>Decompilers are fundamental tools for critical security tasks, from vulnerability discovery to malware analysis, yet their evaluation remains fragmented. Existing approaches primarily focus on syntactic correctness through synthetic micro-benchmarks or subjective human ratings, failing to address real-world requirements for semantic fidelity and analyst usability. We present **DecompileBench**, the first comprehensive framework that enables effective evaluation of decompilers in reverse engineering workflows through three key components: real-world function extraction (comprising 23,400 functions from 130 real-world programs), runtime-aware validation, and automated human-centric assessment using LLM-as-Judge to quantify the effectiveness of decompilers in reverse engineering workflows. Through a systematic comparison between six industrial-strength decompilers and six recent LLM-powered approaches, we demonstrate that LLM-based methods surpass commercial tools in code understandability despite 52.2% lower functionality correctness. These findings highlight the potential of LLM-based approaches to transform human-centric reverse engineering. We open source **DecompileBench** to provide a framework to advance research on decompilers and assist security experts in making informed tool selections based on their specific requirements.</abstract>
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%0 Conference Proceedings
%T DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios
%A Gao, Zeyu
%A Cui, Yuxin
%A Wang, Hao
%A Qin, Siliang
%A Wang, Yuanda
%A Bolun, Zhang
%A Zhang, Chao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F gao-etal-2025-decompilebench
%X Decompilers are fundamental tools for critical security tasks, from vulnerability discovery to malware analysis, yet their evaluation remains fragmented. Existing approaches primarily focus on syntactic correctness through synthetic micro-benchmarks or subjective human ratings, failing to address real-world requirements for semantic fidelity and analyst usability. We present **DecompileBench**, the first comprehensive framework that enables effective evaluation of decompilers in reverse engineering workflows through three key components: real-world function extraction (comprising 23,400 functions from 130 real-world programs), runtime-aware validation, and automated human-centric assessment using LLM-as-Judge to quantify the effectiveness of decompilers in reverse engineering workflows. Through a systematic comparison between six industrial-strength decompilers and six recent LLM-powered approaches, we demonstrate that LLM-based methods surpass commercial tools in code understandability despite 52.2% lower functionality correctness. These findings highlight the potential of LLM-based approaches to transform human-centric reverse engineering. We open source **DecompileBench** to provide a framework to advance research on decompilers and assist security experts in making informed tool selections based on their specific requirements.
%R 10.18653/v1/2025.findings-acl.1194
%U https://aclanthology.org/2025.findings-acl.1194/
%U https://doi.org/10.18653/v1/2025.findings-acl.1194
%P 23250-23267
Markdown (Informal)
[DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://aclanthology.org/2025.findings-acl.1194/) (Gao et al., Findings 2025)
ACL