  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.
