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The most important metric for comparing fuzzing approaches is the amount of flaws the method finds. A simple approach, like random fuzzer, requires virtually no investments, but they can only find 10% of the vulnerabilities hiding in the software. A mutation-based fuzzer can find around 50% of the flaws. Once again the tool investments are minimal, but cost of using the tools and integrating them into the development processes are a lot greater. A fully model-based fuzzer can find as much as 80-90% of the flaws, but it can be the most expensive method to build and maintain. The choice of the tool is often based on integration capabilities and challenges, not coverage. If the protocol is standards-based, a model based fuzzer is often the right choice. But especially with emerging technologies and agile development processes, the specifications needed to create model-based tests are not always available. There might be no consensus on the protocol specification or the specification changes rapidly, or in some special cases the specifications are proprietary and not available to testers. In such cases traffic captures can be used to create mutation-based fuzzers. When the amount of interfaces is vast, random fuzzing might be a budgetary choice to get at least some fuzz test coverage before more advanced capabilities are introduced to the development process.