Automatic boosting of cross-product coverage using Bayesian networks

Dorit Baras, Shai Fine, Laurent Fournier, Dan Geiger, Avi Ziv

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)247-261
Number of pages15
JournalInternational Journal on Software Tools for Technology Transfer
Issue number3
StatePublished - Jun 2011


  • Bayesian networks
  • Coverage
  • Coverage directed generation
  • Functional verification

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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