Bogey testing, also known as zero-failure testing, is used in industry to demonstrate reliability at a high confidence level. This test method is simple to apply; however, it requires excessive test time and/or a large sample size, and thus is often unaffordable. For some products, a failure is defined in terms of a performance characteristic exceeding a specified threshold. For these products, it is possible to measure the performance characteristic at different times during testing. The measurement data can be employed to predict whether or not a test unit will fail by the end of the test. When there are sufficient data to make such a prediction with a high degree of confidence, the test of the unit can be terminated. As a result, the test time is reduced. This paper develops a method for degradation bogey testing to reduce test time. In particular, the paper describes degradation modeling, and the calculation of the conditional failure probability of a test unit. Then we develop the optimum test plans, which choose the sample size, and the expected test time, by minimizing the total test cost, and simultaneously satisfying the constraints on the type II error and available sample size. Sensitivity analysis shows that the optimum test plans are robust against the preestimates of model parameters. The paper also presents decision rules for terminating the test of a unit. The proposed method is illustrated with an example. The application shows that the method is effective in reducing test time.