Constrained random test generation is one of the most widely adopted methods
for generating stimuli for simulation-based verification. Randomness leads to
test diversity, but tests tend to repeatedly exercise the same design logic.
Constraints are written (typically manually) to bias random tests towards
interesting, hard-to-reach, and yet-untested logic. However, as verification
progresses, most constrained random tests yield little to no effect on
functional coverage. If stimuli generation consumes significantly less
resources than simulation, then a better approach involves randomly generating
a large number of tests, selecting the most effective subset, and only
simulating that subset. In this paper, we introduce a novel method for
automatic constraint extraction and test selection. This method, which we call
coverage-directed test selection, is based on supervised learning from coverage
feedback. Our method biases selection towards tests that have a high
probability of increasing functional coverage, and prioritises them for
simulation. We show how coverage-directed test selection can reduce manual
constraint writing, prioritise effective tests, reduce verification resource
consumption, and accelerate coverage closure on a large, real-life industrial
hardware design.

本文介绍一种名为基于覆盖率反馈的测试选择的自动约束提取和测试选择的新方法，通过选择具有高概率增加功能覆盖率的测试，有助于减少手动约束编写，优先考虑有效测试，降低验证资源消耗，加速大型工业硬件设计的覆盖率闭合。