11 papers
ShadowProbe: Language-Extensible Detection of Hidden Algorithmic Complexity Vulnerabilities
Yuanmin Xie, Xiangfan Wu, Wenhao Wu +6
Algorithmic Complexity Vulnerabilities (ACVs) arise when adversarial inputs trigger worst-case execution behavior, causing severe performance degradation or Denial-of-Service condi…
Semantic-aware and Self-improving Program Reduction via Agentic Large Language Models
Xintong Zhou, Hongxu Xu, Chunhao Liao +3
Reducing bug-triggering programs to their minimal essential form is a fundamental task in debugging language processors such as compilers and interpreters. Existing reduction techn…
On the Feasibility of Deduplicating Compiler Bugs with Bisection
Xintong Zhou, Zhenyang Xu, Yongqiang Tian +1
Random testing has proven to be an effective technique for compiler validation. However, the debugging of bugs identified through random testing presents a significant challenge du…
Can Coding Agents Implement Missed Compiler Optimizations? Evaluating LLM Agents on LLVM Peephole Optimizations
Hongxu Xu, Chunhao Liao, Xintong Zhou +1
Coding agents built on large language models are now capable of patching sizable real-world codebases, yet whether they can develop compiler optimizations remains an open question.…
Leveraging Large Language Models for Generalizing Peephole Optimizations
Chunhao Liao, Hongxu Xu, Xintong Zhou +2
Peephole optimizations are a core component of modern optimizing compilers. It rewrites specific instruction into semantically equivalent but more efficient forms. In practice, cre…
LPO: Discovering Missed Peephole Optimizations with Large Language Models
Zhenyang Xu, Hongxu Xu, Yongqiang Tian +2
Peephole optimization is an essential class of compiler optimizations that targets small, inefficient instruction sequences within programs. By replacing such suboptimal instructio…