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machine learning-based test generation

Technique WIKI v1 · 5/26/2026

Machine learning-based test generation is referenced in the evidence as an alternative approach for instruction-stream generation in processor verification. In the cited RISC-V RTL verification context, such approaches are grouped with coverage-guided generation using Bayesian networks and fuzzing, but are described as either not designed for RTL verification, imposing restrictions on generated instruction streams, or not targeting the RISC-V ISA.

Overview

Machine learning-based test generation refers here to a class of alternative test-generation approaches discussed in the context of instruction-stream generation for processor verification. The evidence places it alongside coverage-guided test generation based on Bayesian networks and fuzzing as one of several alternatives to model-based and constraint-solving approaches for processor test generation. [C1]

Use in processor verification context

The cited source discusses test generation for simulation-based processor verification at the Register-Transfer Level (RTL). It states that extensive RTL processor verification is important for avoiding bugs and that simulation-based approaches require efficient test generation to achieve thorough verification. [C2]

Within this landscape, the source identifies several approaches for instruction-stream generation, including model-based methods using constraint solving, optimized constraint propagation across multiple instructions, coverage models for execution paths, Bayesian-network-based coverage guidance, other machine learning techniques, and fuzzing. [C3]

Limitations noted in the evidence

In the specific RISC-V RTL verification discussion, the cited source characterizes the alternative approaches that include Bayesian-network-based coverage guidance, other machine learning techniques, and fuzzing as having limitations: they are described as either not designed for RTL verification, imposing restrictions on generated instruction streams, or not targeting the RISC-V ISA. [C4]

Relationship to RISC-V cross-level testing

The source contrasts these alternatives with a proposed cross-level testing approach for RISC-V processor verification. That approach generates an endless instruction stream without restrictions by evolving the stream during simulation and uses an Instruction Set Simulator as a reference model for the RTL core in a tightly coupled cross-level co-simulation setting. [C5]

Evidence-bounded interpretation

Based only on the provided evidence, machine learning-based test generation should be understood as an acknowledged but not detailed category of test-generation technique in processor verification literature. The evidence does not provide algorithmic details, implementation mechanics, or empirical results for the machine learning techniques themselves; it only reports their existence as alternative approaches and notes limitations in the RISC-V RTL verification setting discussed by the source. [C1] [C4]

LINKED ENTITIES

1 links
Instruction Stream Generation application_area

CITATIONS

5 sources
5 citations
[1] Machine learning techniques are identified as alternative approaches for instruction-stream generation in processor verification. Efficient Cross-Level Testing for Processor Verification: A RISC-V Case-Study
[2] Extensive RTL processor verification is important to avoid bugs, and simulation-based approaches require efficient test generation for thorough verification. Efficient Cross-Level Testing for Processor Verification: A RISC-V Case-Study
[3] The evidence lists model-based constraint-solving approaches, optimized constraint propagation, coverage models, Bayesian-network-based coverage guidance, other machine learning techniques, and fuzzing as approaches related to instruction-stream generation. Efficient Cross-Level Testing for Processor Verification: A RISC-V Case-Study
[4] The cited source states that the alternative approaches including Bayesian networks, other machine learning techniques, and fuzzing are either not designed for RTL verification, impose restrictions on generated instruction streams, or do not target the RISC-V ISA. Efficient Cross-Level Testing for Processor Verification: A RISC-V Case-Study
[5] The contrasted RISC-V cross-level testing approach generates an endless unrestricted instruction stream on the fly and uses an ISS as a reference model in tightly coupled cross-level co-simulation. Efficient Cross-Level Testing for Processor Verification: A RISC-V Case-Study