1. A method comprising, by one or more processors associated with one or more computer systems: receiving information associated with a first plurality of matched image pairs; assessing each matched image pair in the first plurality of matched image pairs to detect a first set of visual pairwise differences that qualify as an error, wherein a visual pairwise difference comprises a difference with respect to at least one of position, size, color, or style information from Cascading Style Sheets (CSS); training, by one or more of the processors, a prediction engine based upon the first plurality of matched image pairs and the first set of visual pairwise differences using a machine learning training module, wherein the prediction engine learns to classify visual pairwise differences based on a decision tree classifier that incorporates one or more algorithms to compute one or more of the differences; receiving information associated with a second plurality of matched image pairs, wherein each matched image pair in the second plurality of matched image pairs comprises at least a portion of a visual end-user experience screen of an event-driven application executed in a client-tier environment; assessing, using the prediction engine, each matched image pair in the second plurality of matched image pairs to detect a second set of visual pairwise differences that qualify as an error; receiving user feedback, for at least one matched image pair in the second plurality of matched image pairs that was assessed to have at least one visual pairwise difference from the second set of visual pairwise differences, that the assessed visual pairwise difference from the second set of visual pairwise differences should not have qualified as an error; and re-training, by one or more of the processors, the prediction engine based on the user feedback.