Using machine learning to improve detection of visual pairwise differences between browsers

One or more computer-readable non-transitory storage media embodying software that is executed to:

• receive information associated with a first plurality of matched image pairs;
• assess 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);
• train 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 computing one or more of the differences;
• receive 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;
• assess, 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;
• receive 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-train the prediction engine based on the user feedback.

A system comprising:

• one or more processors;
• and a memory coupled to the one or more processors comprising instructions executable by the one or more processors, the one or more processors executing the instructions to:
• receive information associated with a first plurality of matched image pairs;
• assess 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);
• train 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 computing one or more of the differences;
• receive 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;
• assess, 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;
• receive 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-train the prediction engine based on the user feedback.