Abstract: User interactions and states of a program development tool utilized to develop an application are tracked. Input features associated with the tracked user interactions and the states are provided to a trained machine learning model to determine a prediction result associated with whether a user is likely unable to proceed in the development of the application and likely needs assistance. In response to a determination that the prediction result at least meets a threshold, one or more resolution suggestions are automatically provided. The one or more resolution suggestions is at least one of: automatically selected based on at least a portion of the tracked user interactions and states, or automatically selected based at least in part the prediction result.

Abstract: A program code component module implementing a portion of a program application is received. A trained machine learning model is used to automatically predict to which one among a plurality of program architecture layer classifications the program code component module belongs. An automatic analysis option is selected based on the predicted program architecture layer classification for the program code component module. The selected automatic analysis option is performed on the program code component module.

Abstract: A repository of graph based visual programming language code instances is analyzed. A similar code portion pattern duplicated is detected among a group of graph based visual programming language code instances included in the repository of graph based visual programming language code instances including by using an index and tokenizing one or more graph nodes connected by one or more graph edges included in a flow corresponding to at least one graph based visual programming language code instance in the group of graph based visual programming language code instances. Within a visual representation of at least one of the group of graph based visual programming language code instances, elements belonging to the detected similar code portion pattern are visually indicated.

Abstract: A specification of a program code component module implementing a portion of a program application is received. A trained machine learning model is used to automatically predict to which one among a plurality of program architecture layer classifications the program code component module belongs. An automatic analysis option is selected based on the predicted program architecture layer classification for the program code component module. The selected automatic analysis option is performed on the program code component module.

Abstract: A system for enabling submission of user feedback includes an extension to an application that is configured for execution on a computing system. The extension is for operation with the application and may be invoked during execution of the application in order to submit user feedback on a feature of the application substantially contemporaneously with a presentation of the feature to the user during execution of the application. The extension may visually identify a location of the feature on a user's screen and display on the user's screen textual input, received from the user, concerning the feature.

Abstract: An integrated model-driven application development and execution environment enables declaration of a data-role in an application model. The data-role is based on a property of a data entity in the application model. The data-role provides for the enforcement of domain-specific security policies with respect to data elements corresponding to the data entity.

Abstract: A computer design model processing system and methods are described that can create visual models of computer systems, store versions of design models in a centralized repository, automatically generate and deploy computer software systems in response to the stored computer design models, define dependencies between computer design models, and automate and assist the development of multiple, possibly dependent, computer design models by multiple developers.