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.

翻译：模拟硬件核查的高效和有效测试具有挑战性。 使用有限的随机测试生成,可能需要数以百万计的测试来实现覆盖目标。 绝大多数的测试无助于覆盖进展,但它们消耗了核查资源。 在本文中,我们建议采用混合智能测试方法,将以前分别处理的两种方法结合起来,即覆盖光化测试选择和新式驱动核查。 覆盖光化测试选择从覆盖反馈到偏向测试学习,以达到最有效的测试。 新发明测试学会识别和模拟与先前的模拟不同的刺激,从而减少模拟数量,提高测试效率。 我们讨论了每种方法的优点和局限性,我们展示了我们的方法如何解决每一种方法的局限性,导致硬件测试既高效又有效。