DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios
Zeyu Gao, Yuxin Cui, Hao Wang, Siliang Qin, Yuanda Wang, Bolun Zhang, Chao Zhang

TL;DR
DecompileBench is a comprehensive benchmarking framework that evaluates decompilers in real-world scenarios, emphasizing semantic fidelity and human usability, and demonstrates the potential of LLM-based approaches in reverse engineering.
Contribution
It introduces the first real-world, systematic evaluation framework for decompilers, incorporating large-scale function extraction, runtime validation, and LLM-based human-centric assessment.
Findings
LLM-based decompilers outperform commercial tools in code understandability.
Despite lower functionality correctness, LLM approaches show promise for reverse engineering.
DecompileBench is open source to facilitate future research and tool selection.
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: \textit{real-world function extraction} (comprising 23,400 functions from 130 real-world programs), \textit{runtime-aware validation}, and \textit{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…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Security and Verification in Computing · Software Testing and Debugging Techniques
MethodsFocus
