28 citations · 51 across the 9 of their papers we have counts for
9 papers
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…
An Empirical Study of Bugs in Data Visualization Libraries
Weiqi Lu, Yongqiang Tian, Xiaohan Zhong +4
Data visualization (DataViz) libraries play a crucial role in presentation, data analysis, and application development, underscoring the importance of their accuracy in transformin…
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…
Unmasking the Genuine Type Inference Capabilities of LLMs for Java Code Snippets
Yiwen Dong, Zhenyang Xu, Yongqiang Tian +1
Type inference is crucial for reusing online code snippets. Although snippets are prevalently shared on platforms like StackOverflow, they often lack essential type information, su…
WDD: Weighted Delta Debugging
Xintong Zhou, Zhenyang Xu, Mengxiao Zhang +2
Delta Debugging is a widely used family of algorithms (e.g., ddmin and ProbDD) to automatically minimize bug-triggering test inputs, thus to facilitate debugging. It takes a list o…
Toward a Better Understanding of Probabilistic Delta Debugging
Mengxiao Zhang, Zhenyang Xu, Yongqiang Tian +2
Given a list L of elements and a property that L exhibits, ddmin is a well-known test input minimization algorithm designed to automatically eliminate irrelevant elements from L. T…
LPR: Large Language Models-Aided Program Reduction
Mengxiao Zhang, Yongqiang Tian, Zhenyang Xu +3
Program reduction is a prevalent technique to facilitate compilers' debugging by automatically minimizing bug-triggering programs. Existing program reduction techniques are either…
Harnessing the Power of LLM to Support Binary Taint Analysis
Puzhuo Liu, Chengnian Sun, Yaowen Zheng +8
This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter…