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Randomized Instruction Stream Generation

Technique

Randomized instruction stream generation is a classical verification technique in which processor test stimuli are produced by randomly creating instruction words that are then executed and compared against a reference model (typically an instruction set simulator, ISS). It is widely used as a baseline for functional processor verification, but suffers from limited effectiveness on corner cases because random streams rarely exercise deep or rarely-taken control paths. Subsequent work has therefore extended it with model-based constraint solving, coverage-guided fuzzing, and coverage-guided aging to systematically push randomized streams toward unexplored processor behavior.

First seen 5/29/2026
Last seen 6/25/2026
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Randomized Instruction Stream Generation

Definition

Randomized instruction stream generation is a processor-level stimuli generation technique in which binary instruction words are produced at random and fed to the design under verification (DUT). The DUT's resulting architectural state (typically register and memory values) is then compared against a reference model, most commonly an instruction set simulator (ISS) of the same instruction set architecture. It is described in the processor verification literature as the classical approach to test program generation, against which more directed methods are measured.

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RELATIONSHIPS

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Employs randomized instruction stream generation to drive cross-level processor verification.
Randomized instruction stream generation is one approach for processor-level stimuli generation.
The paper mentions randomized instruction stream generation as a classical test generation approach.

CITATIONS

6 sources
6 citations — click to expand
[1] Randomized instruction stream generation is the classical baseline technique for processor-level stimuli generation. Processor Verification using Symbolic Execution: A RISC-V Case Study (DATE 2023)
[2] Endless, dynamically evolving randomized instruction streams make cross-level co-simulation complex due to speculative pre-fetching and pipeline behavior. Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing (GLSVLSI 2022)
[3] Coverage-guided fuzzing works by mutating randomly created data and uses coverage feedback, requiring no input model specification. Processor Verification using Symbolic Execution: A RISC-V Case Study (DATE 2023)
[4] Constraint-based / model-based approaches such as Genesys-Pro and constraint-satisfaction random test program generation build on randomized instruction stream generation. Processor Verification using Symbolic Execution: A RISC-V Case Study (DATE 2023)
[5] The RISC-V test sequence generation method based on instruction generation constraints builds on randomized instruction streams by adding instruction-generation constraints. A RISC-V Test Sequences Generation Method Based on Instruction Generation Constraints (Journal of Electronics & Information Technology, 2023)
[6] Cross-level processor verification via endless randomized instruction stream generation with coverage-guided aging (Bruns et al., DATE 2022) uses randomized instruction stream generation as the underlying mechanism for an endless instruction stream. Efficient Cross-Level Processor Verification using Coverage-guided Fuzzing (GLSVLSI 2022), referencing Bruns et al., DATE 2022