Techniques are described for generating HLSD for a textual format source code, which, when rendered, causes a display of visual content. The rendering of the source code generates a tree hierarchy of visual source elements, which logically is possible to map to any graph tree. In an embodiment, visual source elements of the source code are classified to higher-level semantic data (HLSD) labels based on their property(s) and/or the property(s) of neighbor visual source element(s) in the tree hierarchy (context). The HLSD labels indicate the type of HLSD widget mapped to the visual source elements. Techniques further include determining features and a layout arrangement for HLSD widgets and generating a template thereof for the visual content.

Performing usage-based software library decomposition is disclosed herein. In some examples, a processor device generates a first library graph representing a first software library including multiple functions. The first library graph comprises a plurality of nodes that each correspond to a function of the first software library. The processor device identifies a function within the first software library (“invoked function”) that is directly invoked by an application that depends on the first software library, then generates a call graph including nodes within the first library graph (“dependency nodes”) corresponding to either the invoked function or another function invoked by the invoked function during application execution. Using the call graph, the processor device generates a second software library including only functions of the first software library corresponding to dependency nodes of the call graph.

Designers create user experience designs using external design systems. An Experience Design Codification and Management System (EDCMS) retrieves a user experience definition based on the user experience design from the external design system, and generates a comprehensive user experience specification from the user experience definition. Part of the comprehensive user experience specification includes JSON, XML, or YAML code created based on the user experience definition. The EDCMS then packages and encodes the comprehensive user experience specification to create a codified user experience from the comprehensive user experience specification. The codified user experience is then versioned and digitally signed, and the versioned and signed codified user experience is stored in a user experience design repository.

The present invention is a novel system and method for coding, programming and administering a cryptocurrency platform.

Various aspects of this disclosure relate to determining mapping issues in object relational mapping (ORM). An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic.

Capturing dependencies between variables using a variable agnostic object is disclosed. A system is configured to obtain an indication of a first dependency of a first variable to a second variable via a programming interface and depict the first dependency, the first variable, and the second variable in a first instance of a variable agnostic object in a source code. The system is also configured to obtain an indication of a second dependency of a third variable to a fourth variable via the programming interface and depict the second dependency, the third variable, and the fourth variable in a second instance of the variable agnostic object in the source code. The system is also configured to compile the source code to generate a computer-executable program capturing the first dependency and the second dependency based on the first instance and the second instance of the variable agnostic object.

Aspects of the present disclosure are directed to defining an artificial reality environment (XRE) and controlling interactions between various artificial reality actors with a defined XRE schema. The XRE schema includes a set of definitions for an XRE, independent of type of artificial reality device. The definitions in the XRE schema can include standards for both interfaces and data objects. The XRE schema can define XR elements in terms of entities and components of a space, organized according to a hierarchy. Each entity can represent a real or virtual object or space, within the XRE, defined by a name and a collection of one or more components. Each component (as part of an entity) can define aspects and expose information about the entity. The XRE schema can specify structures that that allow actors (e.g., producers, instantiators, and consumers) to define and perform actions in relation to XRE elements.

In some aspects, a computing system can determine a set of attributes based on analyzing input data using attribute templates written in a production-ready programming language. The computing system can generate attribute definitions for the set of attributes using the attribute templates and deploy the attribute definitions for the set of attributes to a production environment of a software program. The software program is written in a programming language compatible with the production-ready programming language. The computing system can monitor the performance of the set of attributes in the production environment of the software program and cause the attribute definitions of the plurality of attributes to be modified based on the monitoring.

The present invention provides a system and method for automation of the creation of a software application as a combination of microservices by extraction of discrete elements of software functionality from a monolithic architectures using dynamic and static analysis of code and non-code-related artefacts; forming them into microservices such that the software application is recreated as serverless hardware infrastructure while also validating the transformed code. In many cases, serverless microservices will significantly reduce the hardware requirement for software systems.

Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.