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Applied Research Overview

Decompiler research that does not neatly fit into one of the fundamental areas is defined here as applied research. Research in this area contributes to a specific use-case of decompilation that may not necessarily improve base decompilation.

As an example, most researchers would agree that variable name prediction in stripped binaries is an important research area1. However, as it stands, variable name prediction does not improve any fundamental research area (except neural decompilation). As such, we consider it an applied research area, with that target being human-comprehensible decompilation.

This section is ever-growing as new research areas are explored in decompilation. Currently, the following areas exist:

  • Symbol Recovery: recovering the names or high-level symbols that are associated with a function or variable
  • Code Similarity: measuring how similar (for various uses) some binary is to another
  • Vulnerability Discovery: tuning decompilation to be better used for vulnerability discovery

Other Research

Some research areas don't have enough work to define a label for them. The following works are listed here:

  • Byte-exact recompilable decompilation4
  • Patchable decompilation2
  • Verifiable decompilation3
  • Higher abstraction support567

  1. Pal, Kuntal Kumar, et al. ""Len or index or count, anything but v1": Predicting Variable Names in Decompilation Output with Transfer Learning." 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2024. ↩

  2. Reiter, Pemma, et al. "Automatically mitigating vulnerabilities in x86 binary programs via partially recompilable decompilation." arXiv preprint arXiv:2202.12336 (2022). ↩

  3. Verbeek, Freek, Pierre Olivier, and Binoy Ravindran. "Sound C Code Decompilation for a subset of x86-64 Binaries." Software Engineering and Formal Methods: 18th International Conference, SEFM 2020, Amsterdam, The Netherlands, September 14–18, 2020, Proceedings 18. Springer International Publishing, 2020. ↩

  4. Schulte, Eric, et al. "Evolving exact decompilation." Workshop on Binary Analysis Research (BAR). 2018. ↩

  5. Fokin, Alexander, et al. "SmartDec: approaching C++ decompilation." 2011 18th Working Conference on Reverse Engineering. IEEE, 2011. ↩

  6. Wu, Ruoyu, et al. "{DnD}: A {Cross-Architecture} deep neural network decompiler." 31st USENIX Security Symposium (USENIX Security 22). 2022. ↩

  7. Liu, Zhibo, et al. "Decompiling x86 deep neural network executables." 32nd USENIX Security Symposium (USENIX Security 23). 2023. ↩