4.6 Article

A Declarative Metamorphic Testing Framework for Autonomous Driving

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

Yao Deng et al.

Summary: The rapid development of artificial intelligence, specifically deep learning technology, has propelled advancements in autonomous driving systems. However, these systems are increasingly threatened by various types of attacks. This survey comprehensively analyzes potential attacks on autonomous driving systems and presents state-of-the-art defense mechanisms, while also suggesting promising research directions for improving safety.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Computer Science, Software Engineering

MeMo: Automatically identifying metamorphic relations in Javadoc comments for test automation

Arianna Blasi et al.

Summary: Software testing relies on effective oracles, with formal specification-based oracles revealing application-specific failures but being costly to obtain and maintain. MeMo is a technique and tool that automatically derives metamorphic equivalence relations from natural language documentation, effectively detecting defects when used as oracles in test cases.

JOURNAL OF SYSTEMS AND SOFTWARE (2021)

Proceedings Paper Computer Science, Software Engineering

Generating Metamorphic Relations for Cyber-Physical Systems with Genetic Programming: An Industrial Case Study

Jon Ayerdi et al.

Summary: This paper presents a case study aiming to automate the generation of Metamorphic Relations (MRs) to solve the test oracle problem. Experimental results show that automatically generated MRs are more cost-effective and outperform manually generated MRs, which can be adopted by industrial practitioners.

PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21) (2021)

Proceedings Paper Computer Science, Software Engineering

TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural Networks

Guanhong Tao et al.

2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2020) (2020)

Proceedings Paper Automation & Control Systems

NeuroDiff: Scalable Differential Verification of Neural Networks using Fine-Grained Approximation

Brandon Paulsen et al.

2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020) (2020)

Proceedings Paper Automation & Control Systems

MARBLE: Model-based Robustness Analysis of Stateful Deep Learning Systems

Xiaoning Du et al.

2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020) (2020)

Proceedings Paper Computer Science, Software Engineering

Few-Shot Guided Mix for DNN Repairing

Xuhong Ren et al.

2020 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2020) (2020)

Article Computer Science, Hardware & Architecture

Metamorphic Testing of Driverless Cars

Zhi Quan Zhou et al.

COMMUNICATIONS OF THE ACM (2019)

Proceedings Paper Computer Science, Software Engineering

DeepStellar: Model-Based Quantitative Analysis of Stateful Deep Learning Systems

Xiaoning Du et al.

ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2019)

Proceedings Paper Computer Science, Software Engineering

DeepMutation: Mutation Testing of Deep Learning Systems

Lei Ma et al.

2018 29TH IEEE INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (2018)

Proceedings Paper Computer Science, Software Engineering

DLFuzz: Differential Fuzzing Testing of Deep Learning Systems

Jianmin Guo et al.

ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2018)

Article Computer Science, Information Systems

Perceptions on the State of the Art in Verification and Validation in Cyber-Physical Systems

Xi Zheng et al.

IEEE SYSTEMS JOURNAL (2017)

Proceedings Paper Computer Science, Theory & Methods

An Empirical Characterization of IFTTT: Ecosystem, Usage, and Performance

Xianghang Mi et al.

PROCEEDINGS OF THE 2017 INTERNET MEASUREMENT CONFERENCE (IMC'17) (2017)

Proceedings Paper Computer Science, Theory & Methods

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Guy Katz et al.

COMPUTER AIDED VERIFICATION, CAV 2017, PT I (2017)

Proceedings Paper Computer Science, Theory & Methods

Safety Verification of Deep Neural Networks

Xiaowei Huang et al.

COMPUTER AIDED VERIFICATION, CAV 2017, PT I (2017)

Article Computer Science, Software Engineering

A Survey on Metamorphic Testing

Sergio Segura et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2016)

Article Computer Science, Software Engineering

METRIC: METamorphic Relation Identification based on the Category-choice framework

Tsong Yueh Chen et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2016)

Article Computer Science, Artificial Intelligence

A study on key technologies of unmanned driving

Xinyu Zhang et al.

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2016)

Article Computer Science, Information Systems

Verifying Cyber-Physical Interactions in Safety-Critical Systems

Sayan Mitra et al.

IEEE SECURITY & PRIVACY (2013)

Article Computer Science, Software Engineering

Testing and validating machine learning classifiers by metamorphic testing

Xiaoyuan Xie et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2011)

Article Computer Science, Artificial Intelligence

Integration testing of context-sensitive middleware-based applications: A metamorphic approach

W. K. Chan et al.

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING (2006)