Shai Fine

Publications (6)0 Total impact

• Article: Automatic boosting of cross-product coverage using Bayesian networks.

  Dorit Baras, Shai Fine, Laurent Fournier, Dan Geiger, Avi Ziv
  STTT. 01/2011; 13:247-261.
• Article: Using Bayesian networks and virtual coverage to hit hard-to-reach events.

  Shai Fine, Laurent Fournier, Avi Ziv
  STTT. 01/2009; 11:291-305.
• Conference Proceeding: Probabilistic Alternative Regression Suites.

  Shady Copty, Shai Fine, Shmuel Ur, Avi Ziv
  International Symposium on Leveraging Applications of Formal Methods, ISoLA 2004, October 30 - November 2, 2004, Paphos, Cyprus. Preliminary proceedings; 01/2004
• Conference Proceeding: Probabilistic regression suites for functional verification.

  Shai Fine, Shmuel Ur, Avi Ziv
  Proceedings of the 41th Design Automation Conference, DAC 2004, San Diego, CA, USA, June 7-11, 2004; 01/2004
• Source

  Available from: psu.edu

  Article: Coverage Directed Test Generation for Functional Verification Using Bayesian Networks

  Shai Fine, Avi Ziv
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  ABSTRACT: Functional verification is widely acknowledged as the bottleneck in the hardware design cycle. This paper addresses one of the main challenges of simulation based verification (or dynamic verification), by providing a new approach for Coverage Directed Test Generation (CDG). This approach is based on Bayesian networks and computer learning techniques. It provides an efficient way for closing a feedback loop from the coverage domain back to a generator that produces new stimuli to the tested design. In this paper, we show how to apply Bayesian networks to the CDG problem. Applying Bayesian networks to the CDG framework has been tested in several experiments, exhibiting encouraging results and indicating that the suggested approach can be used to achieve CDG goals.
  07/2003;
• Article: A probabilistic alternative to regression suites

  Shady Copty, Shai Fine, Shmuel Ur, Elad Yom-Tov, Avi Ziv
  [show abstract] [hide abstract]
  ABSTRACT: Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is the observation that if you run the same test many times, it becomes increasingly less likely to find bugs. To alleviate such problems, a new regression suite practice, using random test generators to create regression suites on-the-fly, is becoming more common. In this practice, instead of maintaining tests, we generate test suites on-the-fly by choosing several specifications and generating a number of tests from each specification.We describe techniques for optimizing random generated test suites. We first show how the set cover greedy algorithms, commonly used for selecting tests for regression suites, may be adapted to selecting specifications for randomly generated regression suites. We then introduce a new class of greedy algorithms, referred to as future-aware greedy algorithms. The algorithms are computationally efficient and generate more effective regression suites.
  Theoretical Computer Science.