Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion generation. While the simulation data is inherently incomplete, it is necessary to evaluate the truth values of the mined assertions. This paper presents an approach to evaluating and constraining hardware assertions with absent scenarios. A Belief-failRate metric is proposed to predict the truth/falseness of generated assertions. By considering both the occurrences of free variable assignments and the conflicts of absent scenarios, we use the metric to sort true assertions in higher ranking and false assertions in lower ranking. Our Belief-failRate guided assertion constraining method leverages the quality of generated assertions. The experimental results show that the Belief-failRate framework performs better than the existing methods. In addition, the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.

中文翻译：

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在不存在的情况下评估和约束硬件断言

在硬件设计验证中挖掘黄金模型的仿真数据是一种有效的断言生成解决方案。虽然模拟数据本质上是不完整的，但有必要评估挖掘断言的真值。本文提出了一种在不存在场景的情况下评估和约束硬件断言的方法。提出了一个 Belief-failRate 指标来预测生成的断言的真/假。通过同时考虑自由变量赋值的发生和不存在场景的冲突，我们使用该指标对排名较高的真断言和较低排名的假断言进行排序。我们的 Belief-failRate 引导断言约束方法利用了生成断言的质量。实验结果表明，Belief-failRate 框架的性能优于现有方法。此外，与以前的方法相比，断言评估和约束程序可以找到更多涵盖新设计功能的断言。