13 citations · 18 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study
Ira Ceka, Hailie Mitchell, Saurabh Pujar +5
Automated Program Repair (APR) agents leverage large language models (LLMs) to autonomously diagnose and patch software bugs using planning, reasoning, and tools. Although these ag…
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…
EditLord: Learning Code Transformation Rules for Code Editing
Weichen Li, Albert Jan, Baishakhi Ray +3
Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original co…
CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation
Jinjun Peng, Leyi Cui, Kele Huang +2
Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrec…
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…
CYCLE: Learning to Self-Refine the Code Generation
Yangruibo Ding, Marcus J. Min, Gail Kaiser +1
Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement…
Vulnerability Detection with Code Language Models: How Far Are We?
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim +6
In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our anal…