Abstract: An approach is provided that receives, over a computer network, transaction data from a number of clients that are running an app. The approach generates association rules by inputting the transaction data to an association rule learning algorithm, such as an Apriori algorithm. Each association rule is based on a user transaction pattern and a desired result, and each association rule includes a generated confidence value that pertains to an expected performance of one of the steps included in the respective association rule. The app is then modified based on an analysis of the generated confidence values, with the app modification being directed towards improving one or more of the confidence values.

Abstract: An approach is provided that receives transaction data from a client that is running an app. The received transaction data identifies a latest step performed by the client. The received transaction data is compared to rules that have been generated from previous inputs of transaction data to an association rule learning algorithm. Each of the rules include steps and a confidence value corresponding to a last step of each of the respective rules. The approach identifies, based on the comparison, some rules where the latest step performed by the client is the penultimate step of the rules. One of the rules is selected based on the confidence value of the respective rule. The process then transmits a message to the client to perform the last step of the selected rule.

Abstract: An approach is provided that receives, over a computer network, transaction data from a number of clients that are running an app. The approach generates association rules by inputting the transaction data to an association rule learning algorithm, such as an Apriori algorithm. Each association rule is based on a user transaction pattern and a desired result, and each association rule includes a generated confidence value that pertains to an expected performance of one of the steps included in the respective association rule. The app is then modified based on an analysis of the generated confidence values, with the app modification being directed towards improving one or more of the confidence values.

Abstract: An approach is provided that receives transaction data from a client that is running an app. The received transaction data identifies a latest step performed by the client. The received transaction data is compared to rules that have been generated from previous inputs of transaction data to an association rule learning algorithm. Each of the rules include steps and a confidence value corresponding to a last step of each of the respective rules. The approach identifies, based on the comparison, some rules where the latest step performed by the client is the penultimate step of the rules. One of the rules is selected based on the confidence value of the respective rule. The process then transmits a message to the client to perform the last step of the selected rule.

Abstract: In gesture reporting and replay, touch events are captured from an operating system of a client device for touch actions on a touchscreen. The touch events are interpreted as gestures. Any application events are captured from an application in response to the gestures. A given gesture and any given application event in response to the given gesture are compared against an expected application event. When there is a match, the given gesture is identified as a responsive gesture. When there is no match, the given gesture is identified as a non-responsive gesture. The gesture information for the given gesture, any application event in response to the given gesture, and a screen layout displayed on the touchscreen at the time of the given gesture are sent to a service. For the information received, the service replays the given gesture and generates a report.

Abstract: In gesture reporting and replay, touch events are captured from an operating system of a client device for touch actions on a touchscreen. The touch events are interpreted as gestures. Any application events are captured from an application in response to the gestures. A given gesture and any given application event in response to the given gesture are compared against an expected application event. When there is a match, the given gesture is identified as a responsive gesture. When there is no match, the given gesture is identified as a non-responsive gesture. The gesture information for the given gesture, any application event in response to the given gesture, and a screen layout displayed on the touchscreen at the time of the given gesture are sent to a service. For the information received, the service replays the given gesture and generates a report.

Abstract: In gesture reporting and replay, touch events are captured from an operating system of a client device for touch actions on a touchscreen. The touch events are interpreted as gestures. Any application events are captured from an application in response to the gestures. A given gesture and any given application event in response to the given gesture are compared against an expected application event. When there is a match, the given gesture is identified as a responsive gesture. When there is no match, the given gesture is identified as a non-responsive gesture. The gesture information for the given gesture, any application event in response to the given gesture, and a screen layout displayed on the touchscreen at the time of the given gesture are sent to a service. For the information received, the service replays the given gesture and generates a report.

Abstract: In gesture reporting and replay, touch events are captured from an operating system of a client device for touch actions on a touchscreen. The touch events are interpreted as gestures. Any application events are captured from an application in response to the gestures. A given gesture and any given application event in response to the given gesture are compared against an expected application event. When there is a match, the given gesture is identified as a responsive gesture. When there is no match, the given gesture is identified as a non-responsive gesture. The gesture information for the given gesture, any application event in response to the given gesture, and a screen layout displayed on the touchscreen at the time of the given gesture are sent to a service. For the information received, the service replays the given gesture and generates a report.