Abstract: An adaptive delay learning algorithm is presented that reduces the amount of delay before making test measurements in an automated test that requires a delay of any type to be completed before a measurement is made in order to remove the possibility that a tester component lying in the measurement path has not achieved a ready state. In the execution of an automated test, a current delay time is set to an initial delay value. Test execution does not begin until the current delay time elapses. If, upon execution, the test fails, the current delay time is set to a different delay time, and the test is reexecuted only after the updated current delay time has elapsed.

Abstract: An adaptive delay learning algorithm is presented that reduces the amount of delay before making test measurements in an automated test that requires a delay of any type to be completed before a measurement is made in order to remove the possibility that a tester component lying in the measurement path has not achieved a ready state. In the execution of an automated test, a current delay time is set to an initial delay value. Test execution does not begin until the current delay time elapses. If, upon execution, the test fails, the current delay time is set to a different delay time, and the test is reexecuted only after the updated current delay time has elapsed.

Abstract: A method and system is presented for optimizing the number of required measurements to obtain in real-time by an automated tester that tests a device under test. During a test run, historical measurements are obtained and tracked. Each subsequent iteration the test attempts to re-measure the fewest number of measurements in real-time to obtain an accurate reflection of whether the device being tested passes or fails based on the real-time measurements and substituting historical measurements for those required measurements that were not taken in real-time.