1 citations · 1 across the 4 of their papers we have counts for
5 papers
Faultless: A Program Equivalence Technique for Validating and Evaluating Neural Decompilers
Luke Dramko, Claire Le Goues, Edward Schwartz
Neural decompilers are machine learning models which perform the process of decompilation, lifting code from a lower-level language to a higher one. Neural decompilers offer substa…
Decaf: Improving Neural Decompilation with Automatic Feedback and Search
Alexander Shypula, Osbert Bastani, Edward Schwartz
Decompilers are useful tools used in reverse engineering to understand compiled source code. Reconstructing source code from compiled binaries is a challenging task, because high-l…
Idioms: Neural Decompilation With Joint Code and Type Definition Prediction
Luke Dramko, Claire Le Goues, Edward J. Schwartz
Decompilers are important tools for reverse engineers that help them analyze software at a higher level of abstraction than assembly code. Unfortunately, because compilation is los…
Fast, Fine-Grained Equivalence Checking for Neural Decompilers
Luke Dramko, Claire Le Goues, Edward J. Schwartz
Neural decompilers are machine learning models that reconstruct the source code from an executable program. Critical to the lifecycle of any machine learning model is an evaluation…
STRIDE: Simple Type Recognition In Decompiled Executables
Harrison Green, Edward J. Schwartz, Claire Le Goues +1
Decompilers are widely used by security researchers and developers to reverse engineer executable code. While modern decompilers are adept at recovering instructions, control flow,…