We summarize an experimental performance evaluation of WASSP and the Heidelberger-Welch (HW) algorithm, two sequential spectral procedures for steady-state simulation analysis. Both procedures approximate the log-smoothed-periodogram of the batch means after suitable data-truncation to eliminate the effects of initialization bias, finally delivering a confidence-interval estimator for the mean response that satisfies user-specified half-length and coverage-probability requirements. HW uses a Cramér-von Mises test for initialization bias based on the method of standardized time series; and then HW fits a quadratic polynomial to the batch-means log-spectrum. In contrast WASSP uses the von Neumann randomness test and the Shapiro-Wilk normality test to obtain an approximately stationary Gaussian batch-means process whose log-spectrum is approximated via wavelets. Moreover, unlike HW, WASSP estimates the final sample size required to satisfy the user's confidence-interval requirements. Regarding closeness of conformance to both confidence-interval requirements, we found that WASSP outperformed HW in the given test problems.