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A variety of expensive software maintenance and testing tasks require a comparison of the behaviors of program versions. Program spectra have recently been proposed as a heuristic for use in pcrforrning such comparisons. To assess t,lrc potential 11sefulness of spectra in this cont,ext, we conducted an experiment that examined the relationship bct,wccn program spectra and program behavior, and empirically compared several types of spectra. This paper reports the results of that experiment,.

A variety of expensive software maintenance and testing tasks require a comparison of the behaviors of program versions. Program spectra have recently been proposed as a heuristic for use in pcrforrning such comparisons. To assess t,lrc potential 11sefulness of spectra in this cont,ext, we conducted an experiment that examined the relationship bct,wccn program spectra and program behavior, and empirically compared several types of spectra. This paper reports the results of that experiment,.

Several data-driven soft sensors have been applied for online quality prediction in polymerization processes. However, industrial data samples often follow a non-Gaussian distribution and contain some outliers. Additionally, a single model is insufficient to capture all of the characteristics in multiple grades. In this study, the support vector clustering (SVC)-based outlier detection method was first used to better handle the nonlinearity and non-Gaussianity in data samples. Then, SVC was integrated into the just-in-time Gaussian process regression (JGPR) modeling method to enhance the prediction reliability. A similar data set with fewer outliers was constructed to build a more reliable local SVC-JGPR prediction model. Moreover, an ensemble strategy was proposed to combine several local SVC-JGPR models with the prediction uncertainty. Finally, the historical data set was updated repetitively in a reasonable way. The prediction results in the industrial polymerization process show the superiority of the proposed method in terms of prediction accuracy and reliability.

No abstract