5 citations · 12 across the 12 of their papers we have counts for
12 papers
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…
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +5
Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant losses in resources consumed by software maintenance. Rece…
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,…
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Aidan Z. H. Yang, Haoye Tian, He Ye +2
Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanc…
Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models
Aidan Z. H. Yang, Sophia Kolak, Vincent J. Hellendoorn +2
Language models have improved by orders of magnitude with the recent emergence of Transformer-based Large Language Models (LLMs). LLMs have demonstrated their ability to generate n…
What is a "bug"? On subjectivity, epistemic power, and implications for software research
David Gray Widder, Claire Le Goues
Considerable effort in software research and practice is spent on bugs. Finding, reporting, tracking, triaging, attempting to fix them automatically, detecting "bug smells" -these…