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This paper describes a method for extracting functional requirements from tests, where tests take the form of vectors of inputs (supplied to the system) and outputs (produced by the system in response to inputs). The approach uses data-mining techniques to infer invariants from the test data, and an automated-verification technology to determine which of these proposed invariants are indeed invariant and may thus be seen as requirements. Experimental results from a pilot study involving an automotive-electronics application show that using tests that fully cover the structure of the software yield more complete invariants than structurally-agnostic black-box tests.