Sap Se

Jai Vignesh R

Rajendra Kumar

Jhansi Rani B

Disclosed herein are system, method, and computer program product embodiments for validating input requests. In an embodiment, an application program interface (API) can receive a input request from a client device or server. The API can validate the input requests based on client-side or server-side technology implemented by the client device or server. In response to validating a input request from the client device and server, the database's framework can validate the database request before processing the input request. In response to failing to validate an input request at the client device, server, or database level, a machine-learning algorithm can be used to determine how to resolve a possible error, which may have caused the failure to validate the input request.

Input validation API using machine learning and database input validation framework

Disclosed herein are system, method, and computer program product embodiments for providing an automated scripting and testing system. An embodiment operates by receiving an interaction between a user and an application. It is determined that the received interaction corresponds to at least one stored interaction based on a comparison of the received interaction with one or more of a plurality of stored interactions. A count of stored corresponding interactions is incremented. It is determined that the count exceeds a threshold. A scripted interaction with the application is generated based on the received interaction and the at least one stored interaction. The scripted interaction is provided for testing the application.

Automated scripting and testing system

Disclosed herein are system, method, and computer program product embodiments for measuring graphical user interface (GUI) usage, generating visual depictions of the GUI usage, and providing GUI modification recommendations based on the GUI usage. A user interface (UI) usage measurement system may monitor user interactions with an application from one or more user devices. The UI usage measurement system may data such as button clicks or other icon interactions on a GUI. After aggregating this information, the UI usage measurement system may apply machine learning techniques to determine a GUI object recommendation. The recommendation may include the movement of one or more GUI objects to different locations so that the user may more quickly access GUI objects that are more heavily utilized. In this manner, the UI usage measurement system may modify the GUI used by a particular user to access elements of an application.

User interface usage measurement and user interface design using machine learning

Accessibility testing is initiated on UI elements rendered in a web browser. The UI elements are associated with a web application. A document object model (DOM) corresponding to the UI elements is retrieved. The DOM corresponding to the UI elements is parsed to retrieve properties associated with the UI elements. Accessibility testing is performed on the UI elements by applying accessibility rules on the UI elements. Images of one or more UI elements are captured from among the UI elements. The accessibility test is performed on the images by applying accessibility rules on the images in an image-processing unit. A result of the accessibility testing is recorded in a file. The result of the accessibility testing is displayed in runtime of the web application in the web browser.

Growing community of inventors

Chennai, India

Jai Vignesh R