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Coverage analysis is used to monitor the quality of the verification process. Reports provided by coverage tools help users identify areas in the design that have not been adequately tested. Because of their sheer size, the analysis of large coverage models can be an intimidating and time-consuming task. Practically, it can only be done by focusing on specific parts of the model. This paper presents a method for defining views onto the coverage data of cross-product functional coverage models. The proposed method allows users to focus on certain aspects of the coverage data to extract relevant, useful information, thereby improving the quality of the coverage analysis. A number of examples are provided that show how the proposed method improved the verification of actual designs.

We present algorithms that solve the following problem: given three ranges of floating-point numbers R x , R y , R z , a floating-point operation (op), and a rounding-mode (round), generate three floating-point numbersx,ȳ,z such thatx ∈ R x ,ȳ ∈ R y ,z ∈ R z , andz = round(x op y). This problem, although quite simple when dealing with intervals of real numbers, is much more complex when considering ranges of machine numbers. We provide full solutions for add and subtract, and partial solutions for multiply and divide. We use range constraints on the input operands and on the result operand of floating-point instructions to target corner cases when generating test cases for use in verification of floating-point hardware. The algorithms have been implemented in a floating-point test-generator and are currently being used to verify floating-point units of several processors.

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