Constrained Pseudo-Random Stimulus Generation

Concept

Constrained pseudo-random stimulus generation is a topic in the test automation portion of a design verification stimuli generation curriculum. It is listed in lecture material as a subject to be covered in Part II (Test Automation), following Part I's discussion of foundational stimuli generation issues such as dynamic vs. static generation, online vs. offline generation, and test length.

First seen 7/12/2026

Last seen 7/12/2026

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Constrained Pseudo-Random Stimulus Generation

Context in the Curriculum

Constrained pseudo-random stimulus generation is identified in the Stimuli Generation lecture material as a topic belonging to Part II: Test Automation, together with the more general topic of Randomness [1]. This places it after Part I, which surveys foundational issues in stimuli generation, including:

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COMS30026 Design Verification: Stimuli Generation ← uses 100% 1e

Constrained pseudo-random stimulus generation is discussed in Part II of the paper.

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CITATIONS

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[1] Constrained pseudo-random stimulus generation is listed as a topic in Part II: Test Automation of the Stimuli Generation lecture series, together with Randomness. [PDF] Stimuli Generation - GitHub Pages

[2] Part I of the lecture covers foundational issues: number of generators, level of abstraction, online vs. offline generation, dynamic vs. static generation, and test length. [PDF] Stimuli Generation - GitHub Pages

[3] Dynamic test generation is based on reaction, static test generation is based on planning; most generators use dynamic features lightly, reacting only to shallow or stable DUV states such as architectural registers or FIFO occupancy. [PDF] Stimuli Generation - GitHub Pages

[4] Offline generators can use dynamic generation via a reference model whose abstraction level and accuracy govern the information available about the DUV. [PDF] Stimuli Generation - GitHub Pages

[5] Short tests are the shortest tests fulfilling a verification-plan requirement and are easy to create, debug, maintain, and quick to simulate. [PDF] Stimuli Generation - GitHub Pages

[6] Long tests combine many requirements in one test wrapped with initial and end sequences; they need fewer tests, avoid repeating initialization, exercise states far from the initial state, traverse less-traveled paths, and often reach verification targets in unexpected ways. [PDF] Stimuli Generation - GitHub Pages

[7] A naive dynamic stimulus intended to trigger forwarding may fail because intervening events such as pipeline-flushing exceptions can eliminate the interesting condition before the relevant pipeline stage is reached. [PDF] Stimuli Generation - GitHub Pages