The craft of software testing: subsystem testing including object-based and object-oriented testing
The craft of software testing: subsystem testing including object-based and object-oriented testing
User defined coverage—a tool supported methodology for design verification
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
A relational model of data for large shared data banks
Communications of the ACM
Hole analysis for functional coverage data
Proceedings of the 39th annual Design Automation Conference
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Experiences in coverage testing of a Java middleware
SEM '05 Proceedings of the 5th international workshop on Software engineering and middleware
FSM-based transaction-level functional coverage for interface compliance verification
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Distance-guided hybrid verification with GUIDO
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Advanced Analysis Techniques for Cross-Product Coverage
IEEE Transactions on Computers
Reuse and optimization of testbenches and properties in a TLM-to-RTL design flow
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification
Journal of Electronic Testing: Theory and Applications
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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.