Coverage-Directed Test Selection Method for Automatic Test Biasing During Simulation-Based Verification


New research paper titled “Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification” from researchers at University of Bristol and Infineon Technologies.

“Constrained random test generation is one 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 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.”

Find the technical paper here. Published May 2022.

Masamba, Nyasha, Kerstin Eder, and Tim Blackmore. “Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification.” arXiv preprint arXiv:2205.08524 (2022).

Further Reading:
Semiconductor Engineering Systems & Design channel
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The Challenges Of Incremental Verification
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