Bayesian Network-based Coverage-directed Test Generation

Concept

Bayesian Network-based Coverage-directed Test Generation (CDG) is a functional hardware verification technique introduced by Fine and Ziv at DAC 2003, which uses Bayesian networks to guide test generation toward coverage goals. It belongs to the broader family of coverage-directed test generation methods and is cited as foundational work by subsequent randomized and coverage-guided verification approaches.

First seen 6/3/2026

Last seen 6/3/2026

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Bayesian Network-based Coverage-directed Test Generation

Overview

Bayesian Network-based Coverage-directed Test Generation (CDG) is a functional verification technique that uses Bayesian networks to model the relationship between test program parameters and the resulting functional coverage, in order to steer test generation toward under-covered parts of a design's state space. The approach was introduced by S. Fine and A. Ziv in the paper "Coverage directed test generation for functional verification using bayesian networks," presented at the Design Automation Conference (DAC) in 2003, pages 286–291 [9].

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Cross-level processor verification via endless randomized instruction stream generation with coverage-guided aging ← mentions 90% 2e

The paper mentions Bayesian network-based coverage-directed test generation as related work.

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CITATIONS

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[1] Fine and Ziv introduced coverage directed test generation for functional verification using Bayesian networks at DAC 2003, pages 286–291. Cross-Level Processor Verification via Endless Randomized Instruction Stream Generation with Coverage-guided Aging (DATE 2022), Reference [9]

[2] Bayesian network-based CDG is cited among the broader family of coverage-directed and feedback-based test generation works in the DATE 2022 paper's reference list. Cross-Level Processor Verification via Endless Randomized Instruction Stream Generation with Coverage-guided Aging (DATE 2022)

[3] Static randomized test strategies tend to favor specific test state spaces and do not change over time, motivating the use of coverage-directed approaches. Cross-Level Processor Verification via Endless Randomized Instruction Stream Generation with Coverage-guided Aging (DATE 2022)

[4] Coverage-guided Aging provides a more balanced coverage profile than a static random generator for endless instruction streams, with no visible gaps in instruction group combinations. Cross-Level Processor Verification via Endless Randomized Instruction Stream Generation with Coverage-guided Aging (DATE 2022)