Abstract: Embodiments of the present invention provide a system for performing analysis and generating remediation estimations for user interfaces associated with software applications. The system is configured for crawling one or more software applications associated with an entity, identifying one or more user interface elements associated with the one or more software applications, comparing and analyzing the one or more user interface elements with one or more standards stored in a data repository, and generating one or more remediation estimations associated with modifying the one or more user interface elements to meet the one or more standards based on comparing and analyzing the one or more user interface elements with the one or more standards.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.

Abstract: A code identifies a first segment of code included in a first computer program stored on a first repository that matches, within a threshold range, a second segment of code included in a second computer program stored on a second repository. The first segment of code and the second segment of code perform one or more computing tasks. An application programming interface (API) is generated that performs the one or more computing tasks of the first and second segments of code. The first segment of code within the first computer program is replaced with a first call to the generated API. The second segment of code within the second computer program is replaced with a second call to the generated API.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.

Abstract: Aspects of the disclosure relate to a dynamic virtual reality (VR) coaching computing platform. The dynamic VR coaching computing platform may configure a VR coaching model based on model training data. The dynamic VR coaching computing platform may use the VR coaching model and a user profile to select a plurality of VR modules. The dynamic VR coaching computing platform may configure a first VR module of the plurality of VR modules based on one or more user selections. The dynamic VR coaching computing platform may receive real-time feedback during execution of the first VR module. The dynamic VR coaching computing platform may select a second plurality of VR modules based on the first real-time feedback. The dynamic VR coaching computing platform may configure a second VR module of the second plurality of VR modules based on user selections.

Abstract: Aspects of the disclosure relate to a dynamic virtual reality (VR) coaching computing platform. The dynamic VR coaching computing platform may configure a VR coaching model based on model training data. The dynamic VR coaching computing platform may use the VR coaching model and a user profile to select a plurality of VR modules. The dynamic VR coaching computing platform may configure a first VR module of the plurality of VR modules based on one or more user selections. The dynamic VR coaching computing platform may receive real-time feedback during execution of the first VR module. The dynamic VR coaching computing platform may select a second plurality of VR modules based on the first real-time feedback. The dynamic VR coaching computing platform may configure a second VR module of the second plurality of VR modules based on user selections.