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DAC
2008
ACM

Functional test selection based on unsupervised support vector analysis

9 years 11 months ago
Functional test selection based on unsupervised support vector analysis
Extensive software-based simulation continues to be the mainstream methodology for functional verification of designs. To optimize the use of limited simulation resources, coverage metrics are essential to guide the development of effective test suites. Traditional coverage metrics are defined based on either a functional model or a structural model of the design. If our goal is to select a subset of tests from a set of tests, using these coverage metrics require simulation of the entire set before the effectiveness of tests can be compared. In this paper, we propose a novel methodology that estimates the input space covered by a set of tests. We use unsupervised support vector analysis to learn such a space, resulting in a subset of tests that represent the original set of tests. A direct application of this methodology is to select tests before simulation in order to reduce simulation cycles. Consequently, simulation effectiveness can be improved. Experimental results based on appli...
Onur Guzey, Li-C. Wang, Jeremy R. Levitt, Harry Fo
Added 12 Nov 2009
Updated 12 Nov 2009
Type Conference
Year 2008
Where DAC
Authors Onur Guzey, Li-C. Wang, Jeremy R. Levitt, Harry Foster
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