G06F8/33

Code completion of method parameters with machine learning

A code completion tool uses machine learning models to more precisely predict the likelihood of the parameters of a method invocation. A score is computed for each candidate variable that is used to rank the viability of a variable as the intended parameter. The score is a weighted sum of a scope factor, an edit distance factor and a declaration proximity factor. The factors are based on a scope model, a method overload model, and a weight file trained offline on a training set of source code programs utilizing various method invocations.

Code completion of method parameters with machine learning

A code completion tool uses machine learning models to more precisely predict the likelihood of the parameters of a method invocation. A score is computed for each candidate variable that is used to rank the viability of a variable as the intended parameter. The score is a weighted sum of a scope factor, an edit distance factor and a declaration proximity factor. The factors are based on a scope model, a method overload model, and a weight file trained offline on a training set of source code programs utilizing various method invocations.

System model smart object configuration

An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. Project data models defining collections of automation objects and their functional relationships can be stored in a model library for selective inclusion in system projects.

Indexing and accessing source code snippets contained in documents

Systems and methods for indexing and accessing code snippets in repositories. A program graph index is maintained for code snippets within a repository with documents that have at least one code snippet. The program graph index includes a program graph indicating a relationship between program elements within each source code snippet within the documents. A user provided code snippet is received and a target program graph indicating a relationship between program elements within the user provided code snippet is determined and compared to each respective program graph. Based on the comparison, an identified set of documents within the repository of documents is determined that have code snippets with respective program graphs that are also at least a sub-tree of the target program graph. At least one document in the identified set of documents is presented to a user.

Indexing and accessing source code snippets contained in documents

Systems and methods for indexing and accessing code snippets in repositories. A program graph index is maintained for code snippets within a repository with documents that have at least one code snippet. The program graph index includes a program graph indicating a relationship between program elements within each source code snippet within the documents. A user provided code snippet is received and a target program graph indicating a relationship between program elements within the user provided code snippet is determined and compared to each respective program graph. Based on the comparison, an identified set of documents within the repository of documents is determined that have code snippets with respective program graphs that are also at least a sub-tree of the target program graph. At least one document in the identified set of documents is presented to a user.

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.

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.

TOOLCAST MANAGEMENT SYSTEM
20220357931 · 2022-11-10 ·

A system for creating and displaying toolcasts includes a processor and a toolcast management system running on the processor in communication with an underlying system. The toolcast management system to create, display and update at least one toolcast to provide media guidance to a user of the underlying system according to the toolcast management interface with the underlying system and the interaction between the user and the at least one toolcast. The toolcast management system includes an interface module to interface with the underlying system and to detect at least one of: objects, data, activities, and events to be recorded in a recording phase for the at least one toolcast; a toolcast creator to record, generate and edit said at least one toolcast according to at least the output of the interface module and a toolcast player to play said at least one toolcast while interacting with said user.

TOOLCAST MANAGEMENT SYSTEM
20220357931 · 2022-11-10 ·

A system for creating and displaying toolcasts includes a processor and a toolcast management system running on the processor in communication with an underlying system. The toolcast management system to create, display and update at least one toolcast to provide media guidance to a user of the underlying system according to the toolcast management interface with the underlying system and the interaction between the user and the at least one toolcast. The toolcast management system includes an interface module to interface with the underlying system and to detect at least one of: objects, data, activities, and events to be recorded in a recording phase for the at least one toolcast; a toolcast creator to record, generate and edit said at least one toolcast according to at least the output of the interface module and a toolcast player to play said at least one toolcast while interacting with said user.

Coding system and coding method using voice recognition
11494161 · 2022-11-08 ·

The present invention relates to a system and a method for coding. In the present invention, the user may simply process the work of coding using various programming languages on Cloud without a need for the user to use a separate input device, by recognizing oral commands spoken by the user and carrying out the natural language processing comprising morphological analysis, syntactic analysis, semantic analysis, discourse analysis or combinations thereof, and creating and executing the programming code based thereon.