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This paper systematically reviews the evolution of binary decompilation techniques, highlighting the integration of modern machine learning methods into the field. It establishes a comprehensive taxonomy of contemporary methodologies and evaluates trends in metrics, tools, and benchmarks, revealing significant challenges like the lack of reliable ground truth and standardized evaluation frameworks. The findings underscore the need for improved benchmarks to facilitate rigorous comparisons and guide future research directions in decompilation.
The integration of machine learning in binary decompilation reveals critical gaps in evaluation standards that could redefine the field's future.
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.
