A structured methodology for validating and verifying behavioral models is being explored. The methodology is adapted from a set of software test and validation criteria, which are based on control and data flow properties of the behavioral model under test. The test criteria are measured by a set of coverage metrics which indicates the types and the locations of untested portions in the model. The methodology includes an adaptive test data generation technique using neural networks. Given the untested portions in the model, effective test data are generated and identified by neural networks to exercise the untested portions. Tools which will provide designers with a quantitative confidence index at the end of their testing and verification efforts are being developed.
