Augmenting Decompiler Output with Learned Variable Names and Types


Qibin Chen and Jeremy Lacomis, Carnegie Mellon University; Edward J. Schwartz, Carnegie Mellon University Software Engineering Institute; Claire Le Goues, Graham Neubig, and Bogdan Vasilescu, Carnegie Mellon University

Distinguished Paper Award Winner


A common tool used by security professionals for reverse-engineering binaries found in the wild is the decompiler. A decompiler attempts to reverse compilation, transforming a binary to a higher-level language such as C. High-level languages ease reasoning about programs by providing useful abstractions such as loops, typed variables, and comments, but these abstractions are lost during compilation. Decompilers are able to deterministically reconstruct structural properties of code, but comments, variable names, and custom variable types are technically impossible to recover.

In this paper we present DIRTY (DecompIled variable ReTYper), a novel technique for improving the quality of decompiler output that automatically generates meaningful variable names and types. DIRTY is built on a Transformer-based neural network model and is trained on code automatically scraped from repositories on GitHub. DIRTY uses this model to postprocesses decompiled files, recommending variable types and names given their context. Empirical evaluation on a novel dataset of C code mined from GitHub shows that DIRTY outperforms prior work approaches by a sizable margin, recovering the original names written by developers 66.4% of the time and the original types 75.8% of the time.

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@inproceedings {277158,
author = {Qibin Chen and Jeremy Lacomis and Edward J. Schwartz and Claire Le Goues and Graham Neubig and Bogdan Vasilescu},
title = {Augmenting Decompiler Output with Learned Variable Names and Types},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
isbn = {978-1-939133-31-1},
address = {Boston, MA},
pages = {4327--4343},
url = {},
publisher = {USENIX Association},
month = aug,