Hybrid Intelligent Testing in Simulation-Based Verification
Hybrid Intelligent Testing in Simulation-Based Verification
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DOI：
10.1109/aitest55621.2022.00013
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Nyasha Masamba;K. Eder;T. Blackmore
中科院分区：
文献类型：
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作者：
Nyasha Masamba;K. Eder;T. Blackmore
基于仿真的硬件验证的高效且有效的测试具有挑战性。使用约束随机测试生成，可能需要数百万次测试才能实现覆盖目标。绝大多数测试不会对覆盖进度做出贡献，但会消耗验证资源。在本文中，我们提出了一种混合智能测试方法，结合了以前单独处理的两种方法，即覆盖范围导向的测试选择和新颖性驱动的验证。覆盖率导向的测试选择从覆盖率反馈中学习，以偏向测试以实现最有效的测试。新颖性驱动验证学习识别和模拟与先前刺激不同的刺激，从而减少模拟次数并提高测试效率。我们讨论了每种方法的优点和局限性，并展示了我们的方法如何解决每种方法的局限性，从而实现高效且有效的硬件测试。
Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contribute to coverage progress, yet they consume verification resources. In this paper, we propose a hybrid intelligent testing approach combining two methods that have previously been treated separately, namely Coverage-Directed Test Selection and Novelty-Driven Verification. Coverage-Directed Test Selection learns from coverage feedback to bias testing toward the most effective tests. Novelty-Driven Verification learns to identify and simulate stimuli that differ from previous stimuli, thereby reducing the number of simulations and increasing testing efficiency. We discuss the strengths and limitations of each method, and we show how our approach addresses each method’s limitations, leading to hardware testing that is both efficient and effective.