5 papers
Do Coding Agents Understand Why Systems Crash---and Why They Don't?
Chenxi Huang, Haonan Wang, Jinghe Zhang +7
Coding agents are increasingly capable of fixing real-world bugs, yet they still struggle with large systems codebases such as the Linux kernel. To understand why, we test how agen…
TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization
Alex Mathai, Shobini Iyer, Aleksandr Nogikh +4
Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping. However, despite their valu…
Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All
Chenxi Huang, Alex Mathai, Feiyang Yu +7
Repairing system crashes discovered by kernel fuzzers like Syzkaller is a critical yet underexplored challenge in software engineering. While recent works have introduced Large Lan…
kAgent: An execution-guided crash resolution agent for the Linux kernel
Alex Mathai, Chenxi Huang, Suwei Ma +7
Fuzzing frameworks like syzkaller have uncovered thousands of Linux kernel crashes, many of which are critical and security-sensitive. However, the ability to rapidly repair these…
KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution
Alex Mathai, Chenxi Huang, Petros Maniatis +4
Large Language Models (LLMs) are consistently improving at increasingly realistic software engineering (SE) tasks. In real-world software stacks, significant SE effort is spent dev…