Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure-Reference-Cited by-同舟云学术

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Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference On Artificial Intelligence Testing (AITest)
language:
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Author:

Zheng Xuan¹,Eder Kerstin¹,Blackmore Tim²

Affiliation:

1. University of Bristol,Trustworthy Systems Laboratory,Bristol,UK

2. SiFive,Verification Team,Cambridge,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10229222/10229438/10229457.pdf?arnumber=10229457

Reference15 articles.

1. A review of novelty detection

2. Scikit-learn: Machine learning in Python;pedregosa;Journal of Machine Learning Research,2011

3. Reluplex made more practical: Leaky ReLU

4. Novelty-driven verification: Using machine learning to identify novel stimuli and close coverage;tim blackmore;Proceedings of the 2021 Design and Verification Conference (Virtual),0

5. Distance-based confidence score for neural network classifiers;mandelbaum,2017

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1. Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure;2024 IEEE International Test Conference in Asia (ITC-Asia);2024-08-18

2. Are solid-state batteries absolutely more environmentally friendly compared to traditional batteries-analyzing from the footprint family viewpoint;Journal of Cleaner Production;2024-04

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