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20172026
most citedDeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

591 citations · 809 across the 11 of their papers we have counts for

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Showing 2018Show all

6 papers · 1 filter

cs.SE2018★ 51 cited

Efficiently Manifesting Asynchronous Programming Errors in Android Apps

Lingling Fan, Ting Su, Sen Chen +4

Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital…

cs.CR2018★ 13 cited

An Empirical Assessment of Security Risks of Global Android Banking Apps

Sen Chen, Lingling Fan, Guozhu Meng +5

Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial los…

cs.SE2018

Towards Efficient Data-flow Test Data Generation

Ting Su, Chengyu Zhang, Yichen Yan +5

Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are use…

cs.SE2018★ 591 cited

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Lei Ma, Felix Juefei-Xu, Fuyuan Zhang +9

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen…

cs.SE2018★ 16 cited

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

Chengyu Zhang, Yichen Yan, Hanru Zhou +5

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on aut…

cs.SE2018★ 115 cited

Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Lingling Fan, Ting Su, Sen Chen +5

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to f…