Preliminary Study on Noise-Resilient Artificial Neural Networks for On-Chip Test Generation.
Tsutomu InamotoTomoki NishinoSenling WangYoshinobu HigamiHiroshi TakahashiPublished in: GCCE (2022)
Keyphrases
- preliminary study
- test generation
- artificial neural networks
- test cases
- design automation
- test sequences
- symbolic execution
- static analysis
- computational intelligence
- low cost
- neural network
- mutation testing
- quality assurance
- software testing
- signal to noise ratio
- high speed
- back propagation
- knowledge management
- training set
- feature space
- decision trees
- test data generation
- machine learning
- real world
- database