Bayesian Network-Based Test Generation is a coverage-guided test-program generation approach that uses Bayesian networks as the underlying machine learning model to steer the generation of test stimuli toward uncovered portions of the design-under-test. It is positioned among model-based and machine-learning-driven test-generation techniques, alongside other approaches that use other machine-learning methods or fuzzing. The concept is cited as related-work reference [10] within RISC-V compliance and verification literature.

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