[Submitted on 17 May 2022 (

v1

), last revised 16 Oct 2022 (this version, v3)]

Title:Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

View a PDF of the paper titled Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification, by Nyasha Masamba and 2 other authors

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Abstract: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.

Submission history

From: Nyasha Masamba [

view email

]

[v1]

Tue, 17 May 2022 17:49:30 UTC (404 KB)

[v2]

Wed, 22 Jun 2022 16:53:06 UTC (400 KB)

[v3]

Sun, 16 Oct 2022 16:24:52 UTC (400 KB)