Patent classifications
G06F8/35
Reducing semantic errors in code generated by machine learning models
Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.
Automatic and predictive source code generation
A design inspector tool generates secure source code related to stencils and design elements of an architecture diagram. The design inspector tool may retrieve source code from a source code repository that includes source code that is relevant to the stencils and design elements implemented by the design inspector tool. When or after a user modifies the source code, the design inspector tool feeds contextual information associated with the stencils and the design elements into a trained machine learning logic. The trained machine learning logic processes the contextual information to retrieve contextually relevant auto complete secure code suggestions from the source code repository. The contextually relevant auto complete source code suggestions may be presented to the user as an option for replacing or augmenting the modified source code.
Computer Device and Method for Facilitating an Interactive Conversational Session with a Digital Conversational Character in an Augmented Environment
Disclosed herein is a software technology for facilitating an interactive conversational session between a user and a digital conversational character. For instance, in one aspect, the disclosed process may involve two primary phases: (1) an authoring phase that involves a first user accessing a content authoring tool to create a given type of visual conversation application that facilitates interactions between a second user and a digital conversational character in an interactive conversational session, and (2) a rendering phase that involves the second user accessing the created visual conversation application to interact with the digital conversational character in an interactive conversational session. In one implementation, accessing the created visual conversation application may involve detecting an object and identifying information associated with the detected object. The digital conversational character involved in the interactive conversational session may be superimposed onto a real-world environment.
Computer Device and Method for Facilitating an Interactive Conversational Session with a Digital Conversational Character in an Augmented Environment
Disclosed herein is a software technology for facilitating an interactive conversational session between a user and a digital conversational character. For instance, in one aspect, the disclosed process may involve two primary phases: (1) an authoring phase that involves a first user accessing a content authoring tool to create a given type of visual conversation application that facilitates interactions between a second user and a digital conversational character in an interactive conversational session, and (2) a rendering phase that involves the second user accessing the created visual conversation application to interact with the digital conversational character in an interactive conversational session. In one implementation, accessing the created visual conversation application may involve detecting an object and identifying information associated with the detected object. The digital conversational character involved in the interactive conversational session may be superimposed onto a real-world environment.
METHOD AND SYSTEM FOR AUTOMATED REFACTORING OF MAINFRAME BASED BATCH SYSTEMS TO CLOUD NATIVE ENVIRONMENT
A Mainframe batch system have various type of source components and each component has its own purpose and functionality within the environment. Existing refactoring are still dependent on manual skills and competency. A method and system for automated refactoring of mainframe based batch systems to cloud native environment is provided. The present disclosure proposes an intelligent automation model that comprehends every aspect of the existing Mainframe batch system, all its inherent source elements along with its dependencies. With this holistic understanding of all the elements of the Mainframe batch system, the system converts the information within them into a proprietary conceptual model. This model has all the information about the source elements is converted into the target architecture which is cloud native. The combination of the model with the externalizable command based templates (EBCT) aligns the conversion to any target technology feasible.
Application development and extensibility/customization using entity modeling systems and methods
Embodiments of systems and methods disclosed herein provide an application development platform in an enterprise computing environment. More specifically, in certain embodiments, systems and methods are disclosed that enable an application development platform to reuse, extend, and/or customize entity-based applications in an enterprise computing environment. The application development platform can extend an entity to include user configured settings including zero or at least one of a property, a permission, an action, a behavior, or a resource to the entity to generate user customized versions of the entity. The applications may be customized by an end user, while allowing the underlying application to be updated without losing any user customizations.
Application development and extensibility/customization using entity modeling systems and methods
Embodiments of systems and methods disclosed herein provide an application development platform in an enterprise computing environment. More specifically, in certain embodiments, systems and methods are disclosed that enable an application development platform to reuse, extend, and/or customize entity-based applications in an enterprise computing environment. The application development platform can extend an entity to include user configured settings including zero or at least one of a property, a permission, an action, a behavior, or a resource to the entity to generate user customized versions of the entity. The applications may be customized by an end user, while allowing the underlying application to be updated without losing any user customizations.
PROVISIONAL SELECTION DRIVES EDIT SUGGESTION GENERATION
Edit automation enhancements may be implemented in source code editors and other text editors. Provisional selections that indicate user intentions are submitted to a suggestion generator with other edit context information, to improve the quality of generated text suggestions and reduce the cognitive load on users. A provisional selection may include a highlighted completion list entry, or document text targeted by a hovering cursor, or metainformation text targeted by the hovering cursor, for example. An inline grey text suggestion driven by provisional selection may be displayed simultaneously with completion list suggestions that were created without regard to provisional selection. Suggestions driven by provisional selection may be interleaved with existing document text. Suggestions may be accepted fully in one gesture, or in parts. Suggestions may be edited by a user before being accepted, driving further suggestion refinement. Multiple suggestions may be displayed simultaneously, reducing pressure on the suggestion generator.
Model integration tool
Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.
Model integration tool
Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.