186 citations · 592 across the 23 of their papers we have counts for
5 papers · 1 filter
Fuzz4All: Universal Fuzzing with Large Language Models
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian +2
Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language a…
Where to Look When Repairing Code? Comparing the Attention of Neural Models and Developers
Dominik Huber, Matteo Paltenghi, Michael Pradel
Neural network-based techniques for automated program repair are becoming increasingly effective. Despite their success, little is known about why they succeed or fail, and how the…
TraceFixer: Execution Trace-Driven Program Repair
Islem Bouzenia, Yangruibo Ding, Kexin Pei +2
When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior. For example, a va…
LExecutor: Learning-Guided Execution
Beatriz Souza, Michael Pradel
Executing code is essential for various program analysis tasks, e.g., to detect bugs that manifest through exceptions or to obtain execution traces for further dynamic analysis. Ho…
Beware of the Unexpected: Bimodal Taint Analysis
Yiu Wai Chow, Max Schäfer, Michael Pradel
Static analysis is a powerful tool for detecting security vulnerabilities and other programming problems. Global taint tracking, in particular, can spot vulnerabilities arising fro…