The Evolution of Binary Decompilation in the Modern Era: A Taxonomy, Literature Review, and Future Perspectives

2026-08-24

AI Summary

本文通过文献综述和分类法探讨了现代二进制反编译技术的发展，指出了现有评估标准不足的问题，并提出未来研究方向。

Abstract

Decompilation has become a foundational technique in software engineering and security analysis, and it is now advancing through the integration of modern machine learning (ML) approaches. This article presents a systematic review of decompilation studies published over the past decades and develops a comprehensive taxonomy of methodologies employed in contemporary research. We further examine trends in evaluation metrics, tools, and benchmarks used to assess state-of-the-art approaches. Our review reveals key challenges, such as the lack of reliable ground truth and the absence of standardized benchmarks, which hinder rigorous comparison. Finally, we outline future research directions.

Problem

• decompilation
• evaluation metrics
• standardized benchmarks

Innovation

• decompilation
• machine learning
• taxonomy
• evaluation metrics
• standardized benchmarks