06/26/2024, 01:00 PM UTC
基于新颖性的随机测试选择方法：利用神经网络加速功能覆盖闭合Novelty-Based Methods for Random Test Selection. Innovation in Verification
1、文章讨论了利用神经网络在增强随机测试中通过基于新颖性的测试选择来提高功能覆盖率。2、重点介绍了一项研究，其中不同的神经网络方法被应用于指导汽车雷达信号处理单元中配置寄存器值的选择，显著减少了所需的模拟次数。3、结果表明，使用专注于覆盖的神经网络可以实现达到高覆盖水平所需模拟次数的大幅减少，尽管存在变异性。1. The article discusses the use of neural networks in enhancing randomized testing by focusing on novelty-based test selection to improve functional coverage. 2. It highlights a study where different neural network methods were applied to guide the selection of configuration register values in an automotive RADAR signal processing unit, significantly reducing the number of simulations needed. 3. The results show that using a coverage-focused neural network can achieve a substantial reduction in the simulations required to reach high coverage levels, though with variability.---
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