most citedNeuralVis: Visualizing and Interpreting Deep Learning Models

3 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.PL20211 cited

Efficient Path-Sensitive Data-Dependence Analysis

Peisen Yao, Jinguo Zhou, Xiao Xiao +3

This paper presents a scalable path- and context-sensitive data-dependence analysis. The key is to address the aliasing-path-explosion problem via a sparse, demand-driven, and fuse…

cs.CL2021

Indexing Context-Sensitive Reachability

Qingkai Shi, Yongchao Wang, Charles Zhang

Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quad…

cs.SE20191 cited

Conquering the Extensional Scalability Problem for Value-Flow Analysis Frameworks

Qingkai Shi, Rongxin Wu, Gang Fan +1

With an increasing number of value-flow properties to check, existing static program analysis still tends to have scalability issues when high precision is required. We observe tha…

cs.SE20193 cited

NeuralVis: Visualizing and Interpreting Deep Learning Models

Xufan Zhang, Ziyue Yin, Yang Feng +3

Deep Neural Network(DNN) techniques have been prevalent in software engineering. They are employed to faciliatate various software engineering tasks and embedded into many software…

cs.SE2019

DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks

Yang Feng, Qingkai Shi, Xinyu Gao +3

Deep neural networks (DNN) have been deployed in many software systems to assist in various classification tasks. In company with the fantastic effectiveness in classification, DNN…