Automatic Constraint Extraction

Concept

Automatic constraint extraction is a verification concept described in the context of coverage-directed test selection: instead of relying only on manually written constraints to steer constrained-random tests, a supervised-learning method uses coverage feedback to prioritize randomly generated tests that are likely to improve functional coverage.

First seen 5/30/2026

Last seen 6/3/2026

Evidence 1 chunks

Wiki v1

WIKI

Overview

Automatic constraint extraction is described as part of a method for improving simulation-based hardware verification. In constrained-random test generation, tests are randomized to increase diversity, but many tests can repeatedly exercise the same design logic. Constraints are typically written manually to bias random tests toward interesting, hard-to-reach, or not-yet-tested logic. [C1]

The cited work introduces a method for automatic constraint extraction and test selection, called coverage-directed test selection. The method is based on supervised learning from coverage feedback. [C2]

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RELATIONSHIPS

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Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification ← introduces 100% 4e

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

Coverage-Directed Test Selection ← implements 90% 1e

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

LINKED ENTITIES

4 links

Coverage-Directed Test Selection implemented_as_or_part_of The evidence states that the introduced method for automatic constraint extraction and test selection is called coverage-directed test selection.

constrained-random test generation applies_to The evidence frames the problem around constrained random test generation, where constraints are usually written manually.

simulation-based verification used_in The paper title and abstract place the method in simulation-based verification.

Functional Coverage optimizes_for The method uses coverage feedback and prioritizes tests likely to increase functional coverage.

CITATIONS

5 sources

5 citations — click to expand

[1] Constrained-random test generation uses randomness for diversity, but tests may repeatedly exercise the same design logic; constraints are typically written manually to bias tests toward interesting, hard-to-reach, or untested logic. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[2] The paper introduces a method for automatic constraint extraction and test selection called coverage-directed test selection, based on supervised learning from coverage feedback. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[3] As verification progresses, many constrained-random tests yield little or no functional-coverage effect, motivating generation of many random tests followed by selection and simulation of only the most effective subset. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[4] Coverage-directed test selection biases selection toward tests with a high probability of increasing functional coverage and prioritizes them for simulation. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[5] 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