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'''Random test generators''' (often abbreviated RTG) are a subset of [[Generator_(computer_science)|generators]] that are used in [[Functional_verification|functional verification]] of microprocessors. Their primary use lies in providing input stimulus to a [[Device_under_test|device under test]]. ▼
▲'''Random test generators''' (often abbreviated RTG) are a subset of [[
In a [[Logic_simulation|simulation]] / [[Testbench|testbench]] verification environment, the simulator processes input created by the RTG and coverage monitors may used to verify that the generator is properly testing the design.▼
▲In a [[
Random test generators range in scope from simple [[Scripting_language|scripts]] and parameterized [[Macro_(computer_science)|macros]] that can be created in a matter of weeks to full featured systems requiring extensive software development. Random test generators are most often created by the designing organizations.▼
▲Random test generators range in scope from simple [[
== Table Based Generators ==▼
Table based test generators are the simplest RTGs available. Creation of such generators can be accomplished relatively quickly, and maintenance requirements are often low. These generators work by capturing [[
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Static generators are similar to table based generators with the exception that the majority of the instruction, operand and data selection reside in complex [[
▲Static generators are similar to table based generators with the exception that the majority of the instruction, operand and data selection reside in complex [[Procedural_code|procedural code]]. Static generators are capable of producing more random behavior than table based generators, but still have trouble hitting many corner-cases. In addition, the skill level required to create and maintain such a tool rises sharply once this level of sophistication is reached.
== Dynamic Generators ==▼
Dynamic generators incorporate significant knowledge about the architecture being tested. They enhance the ability of less-skilled users to generate complex tests that can hit hard-to-reach corner cases without stumbling on subtle programming pitfalls. This added knowledge, flexibility and ease-of-use is reflected in a more complex generator, and consequently the cost of creating and maintaining the generator are greater than for table-based or static generators.
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* [http://www.haifa.ibm.com/dept/svt/papers/simulation/meth_date99.pdf IBM Genesys Pro]
* [http://www.obsidiansoft.com Obsidian Software RAVEN]
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