3 papers
cs.PL2026
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…
cs.SE2025
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…
cs.LG2025
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…