Publications (17)
Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications
Yongqiang Tian, Wuqi Zhang, Ming Wen +4
Model compression can significantly reduce the sizes of deep neural network (DNN) models, and thus facilitates the dissemination of sophisticated, sizable DNN models, especially fo…
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…
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…
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…
Moving beyond Deletions: Program Simplification via Diverse Program Transformations
Haibo Wang, Zezhong Xing, Zheng Wang +2
To reduce the complexity of software, Developers manually simplify program (known as developer-induced program simplification in this paper) to reduce its code size yet preserving…
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…