This paper presents a modular approach which combines model based verification, pattern matching and machine learning methods in order to achieve a high accuracy over computing time ratio. We utilize pattern recognition technique using a supervised machine learning system (as opposed to pattern matching) to classify the patterns either as failures (hotspots) or non-failures, and we use pattern matching to detect all the outlier misses and false detections in each of the regions (based on the calibration set), which will be added or removed from the set of hotspots later on.

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