Using recurrent neural networks for decompilation 

Originally published on

25th International Conference on Software Analysis, Evolution and Reengineering, SANER 2018, Campobasso, Italy, March 20-23, 2018


Deborah S. Katz, Jason Ruchti and Eric M. Schulte


Decompilation, recovering source code from binary, is useful in many situations where it is necessary to analyze or understand software for which source code is not available. Source code is much easier for humans to read than binary code, and there are many tools available to analyze source code. Existing decompilation techniques often generate source code that is difficult for humans to understand because the generated code often does not use the coding idioms that programmers use. Differences from human-written code also reduce the effectiveness of analysis tools on the decompiled source code. To address the problem of differences between decompiled code and human-written code, we present a novel technique for decompiling binary code snippets using a model based on Recurrent Neural Networks. The model learns properties and patterns that occur in source code and uses them to produce decompilation output. We train and evaluate our technique on snippets of binary machine code compiled from C source code. The general approach we outline in this paper is not language-specific and requires little or no domain knowledge of a language and its properties or how a compiler operates, making the approach easily extensible to new languages and constructs. Furthermore, the technique can be extended and applied in situations to which traditional decompilers are not targeted, such as for decompilation of isolated binary snippets; fast, on-demand decompilation; domain-specific learned decompilation; optimizing for readability of decompilation; and recovering control flow constructs, comments, and variable or function names. We show that the translations produced by this technique are often accurate or close and can provide a useful picture of the snippet’s behavior.

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