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The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02)
An Accurate Coverage Forecasting Model for Behavioral Model Verification
Christchurch, New Zealand
January 29-January 31
ISBN: 0-7695-1453-7
Amjad Hajjar, Colorado State University
Tom Chen, Colorado State University
Statistically forecasting potential returns in terms of code coverage for a given set of test cases (patterns) to be applied to a behavioral model can improve the overall effectiveness of behavioral model verification. In this paper, we present a forecasting model for behavioral VHDL model verification. The statistical assumptions of the proposed model are based on experimental evaluation of probability distribution functions and correlation functions. Results show that the forecasting model is of high accuracy. The prediction error of the proposed forecast model in estimating the probability of new coverage is, at most, 2% from the actual probability of having coverage when predicting 1000 simulation cycles into the future. When the prediction window size increases to 10,000 simulation cycles, the expected error in predicting the probability of having coverage is 13%, at most. The marginal error in predicting the waiting time to coverage is less than +-30% in forecasting 1000 simulation cycles, and at most +-22% in forecasting 10,000 simulation cycles.
Index Terms:
Behavioral model verification, VHDL, Statistical stopping rules
Citation:
Amjad Hajjar, Tom Chen, "An Accurate Coverage Forecasting Model for Behavioral Model Verification," delta, pp.104, The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02), 2002
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