Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Micro architecture coverage directed generation of test programs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Asymptotic Model Selection for Naive Bayesian Networks
The Journal of Machine Learning Research
Enhancing the control and efficiency of the covering process [logic verification]
HLDVT '03 Proceedings of the Eighth IEEE International Workshop on High-Level Design Validation and Test Workshop
Harnessing Machine Learning to Improve the Success Rate of Stimuli Generation
IEEE Transactions on Computers
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
Comprehensive Functional Verification: The Complete Industry Cycle (Systems on Silicon)
Functional Verification Coverage Measurement and Analysis
Functional Verification Coverage Measurement and Analysis
Using virtual coverage to hit hard-to-reach events
HVC'07 Proceedings of the 3rd international Haifa verification conference on Hardware and software: verification and testing
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Microprocessor Verification via Feedback-Adjusted Markov Models
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Coverage-Directed Test Generation Automated by Machine Learning -- A Review
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage at early stages of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method.