Functional Coverage Goals and Randomization Weights
In a constrained random approach, different items can be selected more frequently by using randomization weights. Items with a higher randomization weight are selected more frequently.
In Intelligent Coverage, the same effect can be achieved by using coverage goals. A coverage goal specifies how many times a value must land in a bin before the bin is considered covered. Each bin within the coverage model can have a different coverage goal. By default, coverage goals are also used as a randomization weight. Bins with a higher goal/weight will be generated more frequently. When a bin reaches its goal, it is no longer selected by Intelligent Coverage randomization.
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