Biased random instruction generation is a simulation-based design verification technique in which test programs for a processor or hardware design are constructed by randomly emitting instructions according to a set of bias parameters (e.g., instruction-mix weights, operand distributions, addressing modes) rather than from a uniform distribution. By skewing the probability of selecting each instruction class toward coverage-productive values, biased random instruction generation aims to exercise hard-to-reach corner cases of the design more efficiently than uniform random testing, while still retaining the scalability advantages of random stimulus generation.

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