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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…
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…
Towards Better Linux Kernel Fault Localization: Leveraging Contrastive Reasoning and Hierarchical Context Analysis
Haichi Wang, Ruiguo Yu, Yesong Pang +4
Debugging the Linux kernel remains a formidable challenge due to its vast codebase, complex architecture, and low-level programming intricacies. Effective fault localization (FL) i…