A Study of Outlier Analysis Techniques for Delay Testing

This work provides a survey study of several outlier analysis techniques and compares their effectiveness in the context of delay testing. Three different approaches are studied, an Euclidean-distance based algorithm, random forest, and one-class support vector machine (SVM), from which more advanced methods are derived and analyzed. We conclude that one-class SVM using a polynomial kernel is most effective for detecting delay defects, while keeping overkills minimized. The best models were successfully validated and a feasible approach to delay testing using one-class SVM is proposed.