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The craft of software testing: subsystem testing including object-based and object-oriented testing
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DAC '98 Proceedings of the 35th annual Design Automation Conference
Functional verification methodology for microprocessors using the Genesys test-program generator
DATE '99 Proceedings of the conference on Design, automation and test in Europe
High quality behavioral verification using statistical stopping criteria
Proceedings of the conference on Design, automation and test in Europe
Machine Learning
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Defining coverage views to improve functional coverage analysis
Proceedings of the 41st annual Design Automation Conference
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HLDVT '03 Proceedings of the Eighth IEEE International Workshop on High-Level Design Validation and Test Workshop
Priority directed test generation for functional verification using neural networks
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Proceedings of the 43rd annual Design Automation Conference
Harnessing Machine Learning to Improve the Success Rate of Stimuli Generation
IEEE Transactions on Computers
Advanced Analysis Techniques for Cross-Product Coverage
IEEE Transactions on Computers
Functional test selection based on unsupervised support vector analysis
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EmGen: an automatic test-program generation tool for embedded IP cores
ICESS'04 Proceedings of the First international conference on Embedded Software and Systems
Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification
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One of the main goals of coverage tools is to provide the user with informative presentation of coverage information. Specifically, information on large, cohesive sets of uncovered tasks with common properties is very useful. This paper describes methods for discovering and reporting large uncovered spaces (holes) for cross-product functional coverage models. Hole analysis is a presentation method for coverage data that is both succinct and informative. Using case studies, we show how hole analysis was used to detect large uncovered spaces and improve the quality of verification.