37 papers
Learning Globally Reusable Skills for Coding Agents
Chen Yang, Jiashuo Tian, Ziqi Wang +3
Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evoluti…
Agent-Based Test Assertion Generation via Diverse Perspective Aggregation
Dong Wang, Qiaoyu Han, Lin Yang +3
Test assertions are critical elements of unit tests, serving as checkpoints to validate expected behavior and ensure software correctness. Numerous techniques have been proposed to…
Evaluating Fuzz Testing for Reinforcement Learning Agents
Zhibin Kang, Hanmo You, Dong Wang +2
Reinforcement Learning (RL) agents are increasingly deployed in safety-critical domains such as robotics, autonomous driving, and drone control, where unexpected behaviors may lead…
KernelDiag: Agent-Based Root Cause Diagnosis for Kernel Crashes
Weijing Wang, Zan Wang, Dong Wang +2
The Linux kernel is one of the most complex software systems, where automated fuzzing continuously exposes thousands of crashes, yet root-cause diagnosis remains a manual and time-…
GapForge: Directed Compiler Fuzzing via Coverage-Gap Analysis
Mingxuan Zhu, Qingyuan Liang, Junjie Chen +2
Modern compiler codebases (e.g., GCC and LLVM) are large and complex, making comprehensive coverage across diverse code regions highly challenging. Most existing test generation te…
LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
Zhao Tian, Yingquan Zhao, Chenyao Suo +2
LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large language models (LLMs) have recently…