Coverage-Directed Test Selection

Technique

Coverage-Directed Test Selection is a supervised-learning-based technique for simulation-based hardware verification that learns from coverage feedback to select and prioritize randomly generated tests with a high probability of increasing functional coverage, reducing wasted simulations and supporting faster coverage closure.

First seen 5/30/2026

Last seen 6/3/2026

Evidence 2 chunks

Wiki v2

WIKI

Overview

Coverage-Directed Test Selection is a technique for simulation-based hardware verification. It was introduced to address a common limitation of constrained random test generation: while randomness provides diversity, tests often repeatedly exercise the same design logic, and as verification progresses many tests contribute little or nothing to functional coverage.[1][2]

Core idea

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RELATIONSHIPS

8 connections

Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification ← introduces 100% 3e

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

Hybrid Intelligent Testing in Simulation-Based Verification ← uses 100% 3e

The paper uses Coverage-Directed Test Selection as one of the two combined methods in its hybrid approach.

Coverage Feedback uses → 100% 2e

Coverage-directed test selection leverages coverage feedback to guide the supervised learning process.

Supervised Learning uses → 100% 1e

Coverage-directed test selection is based on supervised learning from coverage feedback.

Automatic Constraint Extraction implements → 90% 1e

Coverage-directed test selection implements automatic constraint extraction as part of its mechanism.

Functional Coverage implements → 90% 1e

Coverage-directed test selection biases test selection towards increasing functional coverage.

Functional Coverage uses → 100% 1e

Coverage-directed test selection biases selection towards tests likely to increase functional coverage.

Hybrid Intelligent Testing ← uses 100% 1e

Hybrid Intelligent Testing combines Coverage-Directed Test Selection with Novelty-Driven Verification.

LINKED ENTITIES

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Hybrid Intelligent Testing in Simulation-Based Verification USES Extracted graph relationship

Hybrid Intelligent Testing USES Extracted graph relationship

CITATIONS

7 sources

7 citations — click to expand

[1] Constrained random test generation provides diversity, but tests tend to repeatedly exercise the same design logic. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[2] As verification progresses, most constrained random tests yield little to no effect on functional coverage. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[3] When stimuli generation consumes significantly less resources than simulation, a better approach is to generate many tests, select the most effective subset, and simulate only that subset. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[4] Coverage-Directed Test Selection is described as a method for automatic constraint extraction and test selection based on supervised learning from coverage feedback. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[5] The method biases selection toward tests that have a high probability of increasing functional coverage and prioritizes them for simulation. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[6] The paper reports that Coverage-Directed Test Selection can reduce manual constraint writing, prioritize effective tests, reduce verification resource consumption, and accelerate coverage closure on a large, real-life industrial hardware design. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[7] Hybrid Intelligent Testing in Simulation-Based Verification combines Coverage-Directed Test Selection with Novelty-Driven Verification, and describes Coverage-Directed Test Selection as learning from coverage feedback to bias testing toward the most effective tests. Hybrid Intelligent Testing in Simulation-Based Verification