1 citations · 1 across the 4 of their papers we have counts for
4 papers
FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them
Harrison Green, Claire Le Goues, Fraser Brown
Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation.…
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…
Mind the Gap: The Difference Between Coverage and Mutation Score Can Guide Testing Efforts
Kush Jain, Goutamkumar Tulajappa Kalburgi, Claire Le Goues +1
An "adequate" test suite should effectively find all inconsistencies between a system's requirements/specifications and its implementation. Practitioners frequently use code covera…