Differential fuzz testing
TechniqueDifferential fuzz testing uses generated inputs to expose behavioral discrepancies. In the common form shown by the cited systems, the same input is run on multiple implementations or on a target and a reference model, and mismatches are investigated as potential bugs; the literature here also includes a looser single-model variant that maximizes prediction differences. Representative applications in the evidence include Ethereum Virtual Machines, deep learning systems, and CPU RTL validation.
WIKI
Differential fuzz testing
Overview
Differential fuzz testing is a fuzzing strategy that treats behavioral differences as the signal of interest. In the common cross-implementation form, the same generated input is executed on multiple implementations, or on a target and a golden/reference model, and discrepancies in outputs or architectural state are reported for triage. The evidence here also shows a broader usage of the term in deep learning, where the objective is to maximize the prediction difference between an original input and a mutated input inside a single model.
Common structure
Across the cited examples, differential fuzz testing typically combines:
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