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

Paper

A 2022 arXiv paper that introduces coverage-directed test selection, a supervised-learning method that uses coverage feedback to prioritize randomly generated verification tests likely to increase functional coverage, with the goal of reducing manual constraint writing, resource use, and time to coverage closure.

First seen 5/30/2026

Last seen 6/3/2026

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Wiki v1

WIKI

Overview

Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification is a paper submitted to arXiv on 17 May 2022 and last revised as version 3 on 16 October 2022. The arXiv record lists Nyasha Masamba and two other authors. The paper addresses test selection for simulation-based hardware verification, where constrained random test generation is widely used to create verification stimuli. [Submission history and authorship; Problem context]

Problem addressed

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RELATIONSHIPS

16 connections

Automatic Constraint Extraction introduces → 100% 4e

The paper introduces a novel method for automatic constraint extraction as part of its coverage-directed test selection approach.

Supervised Learning uses → 100% 4e

The paper's proposed method is based on supervised learning from coverage feedback.

constrained-random test generation uses → 90% 4e

The paper discusses constrained random test generation as the predominant method for stimuli generation that the proposed approach builds upon.

Nyasha Masamba authored by → 100% 4e

The paper is authored by Nyasha Masamba and 2 other authors.

Coverage-Directed Test Selection introduces → 100% 3e

The paper introduces coverage-directed test selection as a novel method for automatic constraint extraction and test selection.

Simulation-Based Verification uses → 100% 3e

The paper addresses test selection within the context of simulation-based verification.

Coverage Feedback uses → 100% 3e

The supervised learning method in the paper is based on learning from coverage feedback.

Coverage Closure evaluates → 100% 3e

The paper demonstrates that its method can accelerate coverage closure on a large industrial hardware design.

Stimuli Generation uses → 95% 2e

The paper discusses stimuli generation as the process from which tests are selected.

Functional Coverage uses → 100% 2e

The paper's method biases test selection towards increasing functional coverage.

Functional Coverage evaluates → 100% 2e

The paper evaluates its method based on its ability to increase functional coverage.

Coverage Closure uses → 100% 2e

The paper demonstrates that coverage-directed test selection can accelerate coverage closure.

constrained-random test generation evaluates → 85% 1e

The paper evaluates and contextualises constrained random test generation as the dominant existing approach that coverage-directed test selection improves upon.

Simulation-Based Verification evaluates → 90% 1e

The paper evaluates coverage-directed test selection within the context of simulation-based verification.

simulation-based verification uses → 100% 1e

The paper addresses test selection within the context of simulation-based verification.

Stimulus Generation mentions → 90% 1e

The paper mentions stimulus generation as the context in which constrained random test generation operates.