Automatic boosting of cross-product coverage using Bayesian networks |
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Authors: | Dorit Baras Shai Fine Laurent Fournier Dan Geiger Avi Ziv |
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Affiliation: | 1.IBM Research Laboratory in Haifa,Haifa,Israel;2.Department of Computer Science,Technion, IIT,Haifa,Israel |
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Abstract: | 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 in the 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. |
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