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DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous
  Cars

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

28 August 2017
Yuchi Tian
Kexin Pei
Suman Jana
Baishakhi Ray
    AAML
ArXivPDFHTML

Papers citing "DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars"

50 / 127 papers shown
Title
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
Cheng Chen
Grant Xiao
Daehyun Lee
Lishan Yang
E. Smirni
H. Alemzadeh
Xugui Zhou
AAML
31
0
0
26 Apr 2025
Towards Assessing Deep Learning Test Input Generators
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi
Ahmed Hajyahmed
Mohamed Elshafei
Foutse Khomh anb Diego Elias Costa
D. Costa
AAML
37
0
0
03 Apr 2025
Online Controller Synthesis for Robot Collision Avoidance: A Case Study
Yuheng Fan
Wang Lin
44
0
0
08 Feb 2025
Path Analysis for Effective Fault Localization in Deep Neural Networks
Path Analysis for Effective Fault Localization in Deep Neural Networks
Soroush Hashemifar
Saeed Parsa
A. Kalaee
AAML
41
0
0
28 Jan 2025
Improving DNN Modularization via Activation-Driven Training
Improving DNN Modularization via Activation-Driven Training
Tuan Ngo
Abid Hassan
Saad Shafiq
Nenad Medvidovic
MoMe
27
0
0
01 Nov 2024
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
Lam Nguyen Tung
Steven Cho
Xiaoning Du
Neelofar Neelofar
Valerio Terragni
Stefano Ruberto
Aldeida Aleti
148
2
0
30 Oct 2024
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Ruben Grewal
Paolo Tonella
Andrea Stocco
48
12
0
29 Apr 2024
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path
  Forward
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
Xuan Xie
Jiayang Song
Zhehua Zhou
Yuheng Huang
Da Song
Lei Ma
OffRL
50
6
0
12 Apr 2024
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Shahin Atakishiyev
Mohammad Salameh
Randy Goebel
69
6
0
18 Mar 2024
Adaptive Testing Environment Generation for Connected and Automated
  Vehicles with Dense Reinforcement Learning
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
Jingxuan Yang
Ruoxuan Bai
Haoyuan Ji
Yi Zhang
Jianming Hu
Shuo Feng
32
3
0
29 Feb 2024
Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep
  Learning Projects
Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
Han Wang
Sijia Yu
Chunyang Chen
Burak Turhan
Xiaodong Zhu
ELM
MLAU
23
2
0
26 Feb 2024
Machine Translation Testing via Syntactic Tree Pruning
Machine Translation Testing via Syntactic Tree Pruning
Quanjun Zhang
Juan Zhai
Chunrong Fang
Jiawei Liu
Dongrui Liu
Haichuan Hu
Qingyu Wang
23
3
0
01 Jan 2024
Sensitive Region-based Metamorphic Testing Framework using Explainable
  AI
Sensitive Region-based Metamorphic Testing Framework using Explainable AI
Yuma Torikoshi
Y. Nishi
Juichi Takahashi
25
2
0
14 Mar 2023
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep
  Neural Networks
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Zohreh Aghababaeyan
Manel Abdellatif
Mahboubeh Dadkhah
Lionel C. Briand
AAML
28
15
0
08 Mar 2023
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep
  Neural Networks
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Xingwu Guo
Ziwei Zhou
Yueling Zhang
Guy Katz
M. Zhang
AAML
37
5
0
27 Jan 2023
Understanding the Complexity and Its Impact on Testing in ML-Enabled
  Systems
Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems
Junming Cao
Bihuan Chen
Longjie Hu
Jie Ying Gao
Kaifeng Huang
Xin Peng
21
3
0
10 Jan 2023
FedDebug: Systematic Debugging for Federated Learning Applications
FedDebug: Systematic Debugging for Federated Learning Applications
Waris Gill
A. Anwar
Muhammad Ali Gulzar
FedML
31
11
0
09 Jan 2023
When and Why Test Generators for Deep Learning Produce Invalid Inputs:
  an Empirical Study
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study
Vincenzo Riccio
Paolo Tonella
AAML
24
29
0
21 Dec 2022
QEBVerif: Quantization Error Bound Verification of Neural Networks
QEBVerif: Quantization Error Bound Verification of Neural Networks
Yedi Zhang
Fu Song
Jun Sun
MQ
18
11
0
06 Dec 2022
An Empirical Study of Library Usage and Dependency in Deep Learning
  Frameworks
An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks
Mohamed Raed El aoun
L. Tidjon
Ben Rombaut
Foutse Khomh
Ahmed E. Hassan
22
5
0
28 Nov 2022
DriveFuzz: Discovering Autonomous Driving Bugs through Driving
  Quality-Guided Fuzzing
DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing
Seulbae Kim
Major Liu
J. Rhee
Yuseok Jeon
Yonghwi Kwon
C. Kim
27
42
0
25 Oct 2022
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep
  Neural Networks
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
Jiyang Guan
Jian Liang
Ran He
AAML
MLAU
42
29
0
21 Oct 2022
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Shengming Yuan
Qilong Zhang
Lianli Gao
Yaya Cheng
Jingkuan Song
AAML
24
42
0
05 Oct 2022
Perception Simplex: Verifiable Collision Avoidance in Autonomous
  Vehicles Amidst Obstacle Detection Faults
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
Ayoosh Bansal
Hunmin Kim
Simon Yu
Bo-wen Li
N. Hovakimyan
Marco Caccamo
L. Sha
AAML
32
4
0
04 Sep 2022
Verifiable Obstacle Detection
Verifiable Obstacle Detection
Ayoosh Bansal
Hunmin Kim
Simon Yu
Bo-Yi Li
N. Hovakimyan
Marco Caccamo
L. Sha
23
6
0
30 Aug 2022
Mixed-Precision Neural Networks: A Survey
Mixed-Precision Neural Networks: A Survey
M. Rakka
M. Fouda
Pramod P. Khargonekar
Fadi J. Kurdahi
MQ
23
11
0
11 Aug 2022
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN
  Supervision Testing
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
Michael Weiss
A. Gómez
Paolo Tonella
AAML
18
6
0
21 Jul 2022
Guiding the retraining of convolutional neural networks against
  adversarial inputs
Guiding the retraining of convolutional neural networks against adversarial inputs
Francisco Durán
Silverio Martínez-Fernández
Michael Felderer
Xavier Franch
AAML
30
1
0
08 Jul 2022
Abstraction and Refinement: Towards Scalable and Exact Verification of
  Neural Networks
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks
Jiaxiang Liu
Yunhan Xing
Xiaomu Shi
Fu Song
Zhiwu Xu
Zhong Ming
18
10
0
02 Jul 2022
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal
  Verification Perspective
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Mark Huasong Meng
Guangdong Bai
Sin Gee Teo
Zhe Hou
Yan Xiao
Yun Lin
J. Dong
AAML
21
43
0
24 Jun 2022
A Search-Based Testing Approach for Deep Reinforcement Learning Agents
A Search-Based Testing Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian
Manel Abdellatif
Lionel C. Briand
M. Bagherzadeh
Ramesh S
39
27
0
15 Jun 2022
AEON: A Method for Automatic Evaluation of NLP Test Cases
AEON: A Method for Automatic Evaluation of NLP Test Cases
Jen-tse Huang
Jianping Zhang
Wenxuan Wang
Pinjia He
Yuxin Su
Michael R. Lyu
37
23
0
13 May 2022
Search-Based Testing of Reinforcement Learning
Search-Based Testing of Reinforcement Learning
Martin Tappler
Filip Cano Córdoba
B. Aichernig
Bettina Könighofer
ELM
OffRL
21
23
0
07 May 2022
Software Testing for Machine Learning
Software Testing for Machine Learning
D. Marijan
A. Gotlieb
AAML
22
27
0
30 Apr 2022
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a
  Pedestrian Automatic Emergency Brake System
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
Markus Borg
Jens Henriksson
Kasper Socha
Olof Lennartsson
Elias Sonnsjo Lonegren
T. Bui
Piotr Tomaszewski
S. Sathyamoorthy
Sebastian Brink
M. H. Moghadam
24
23
0
16 Apr 2022
Characterizing and Understanding the Behavior of Quantized Models for
  Reliable Deployment
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
Qiang Hu
Yuejun Guo
Maxime Cordy
Xiaofei Xie
Wei Ma
Mike Papadakis
Yves Le Traon
MQ
36
1
0
08 Apr 2022
LaF: Labeling-Free Model Selection for Automated Deep Neural Network
  Reusing
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Qiang Hu
Yuejun Guo
Maxime Cordy
Xiaofei Xie
Mike Papadakis
Yves Le Traon
23
5
0
08 Apr 2022
Testing Feedforward Neural Networks Training Programs
Testing Feedforward Neural Networks Training Programs
Houssem Ben Braiek
Foutse Khomh
AAML
11
14
0
01 Apr 2022
Exploring ML testing in practice -- Lessons learned from an interactive
  rapid review with Axis Communications
Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications
Qunying Song
Markus Borg
Emelie Engström
H. Ardö
Sergio Rico
14
10
0
30 Mar 2022
SoK: On the Semantic AI Security in Autonomous Driving
SoK: On the Semantic AI Security in Autonomous Driving
Junjie Shen
Ningfei Wang
Ziwen Wan
Yunpeng Luo
Takami Sato
...
Zhenyu Zhong
Kang Li
Ziming Zhao
Chunming Qiao
Qi Alfred Chen
AAML
15
39
0
10 Mar 2022
MUC-driven Feature Importance Measurement and Adversarial Analysis for
  Random Forest
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
Shucen Ma
Jianqi Shi
Yanhong Huang
Shengchao Qin
Zhe Hou
AAML
24
4
0
25 Feb 2022
Testing Deep Learning Models: A First Comparative Study of Multiple
  Testing Techniques
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
M. K. Ahuja
A. Gotlieb
Helge Spieker
AAML
11
4
0
24 Feb 2022
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework
  Based on Excitable Neurons
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Haibo Jin
Ruoxi Chen
Haibin Zheng
Jinyin Chen
Yao Cheng
Yue Yu
Xianglong Liu
AAML
25
6
0
12 Feb 2022
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network
  Accelerators
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Lois Orosa
Skanda Koppula
Yaman Umuroglu
Konstantinos Kanellopoulos
Juan Gómez Luna
Michaela Blott
K. Vissers
O. Mutlu
38
4
0
04 Feb 2022
Mind the Gap! A Study on the Transferability of Virtual vs
  Physical-world Testing of Autonomous Driving Systems
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems
Andrea Stocco
Brian Pulfer
Paolo Tonella
27
67
0
21 Dec 2021
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
Zohreh Aghababaeyan
Manel Abdellatif
Lionel C. Briand
Ramesh S
M. Bagherzadeh
AAML
37
44
0
20 Dec 2021
DeepDiagnosis: Automatically Diagnosing Faults and Recommending
  Actionable Fixes in Deep Learning Programs
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Mohammad Wardat
Breno Dantas Cruz
Wei Le
Hridesh Rajan
22
52
0
07 Dec 2021
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and
  Distribution-Aware Criterion
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
Yuanyuan Yuan
Qi Pang
Shuai Wang
35
21
0
03 Dec 2021
Understanding Performance Problems in Deep Learning Systems
Understanding Performance Problems in Deep Learning Systems
Junming Cao
Bihuan Chen
Chao Sun
Longjie Hu
Shuai Wu
Xin Peng
30
27
0
03 Dec 2021
A Survey on Scenario-Based Testing for Automated Driving Systems in
  High-Fidelity Simulation
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
Ziyuan Zhong
Yun Tang
Yuan Zhou
V. Neves
Yang Liu
Baishakhi Ray
43
60
0
02 Dec 2021
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