AI 中文总结
本文对反编译相关研究进行系统综述，构建了当代反编译方法论的全面分类法，分析了评估指标等的发展趋势，指出其面临基准真值与标准化基准缺失的挑战，并展望了未来研究方向。
AI 中文摘要
反编译已成为软件工程与安全分析领域的基础技术，目前正通过融合现代机器学习（ML）方法取得进展。本文对过去数十年发表的反编译研究开展系统综述，并针对当代研究中采用的方法论构建了全面的分类法。我们进一步分析了用于评估最新方法的评估指标、工具及基准的发展趋势。本综述揭示了若干关键挑战，例如缺乏可靠的基准真值与标准化基准，这阻碍了严谨的对比研究。最后，我们概述了未来的研究方向。
英文摘要
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.
