Web: http://arxiv.org/abs/2205.08524

June 23, 2022, 1:11 a.m. | Nyasha Masamba, Kerstin Eder, Tim Blackmore

cs.LG updates on arXiv.org arxiv.org

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 …

ar arxiv learning simulation supervised learning test verification

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