Various embodiments, methods and systems for implementing a distributed computing system scene assembly engine are provided. Initially, a selection of a first synthetic data asset and a selection of a second synthetic data asset are received from a distributed synthetic data as a service (SDaaS) integrated development environment (IDE). A synthetic data asset is associated with asset-variation parameters and scene-variation parameters, the asset-variation parameters and scene-variation parameters are programmable for machine-learning. Values for generating a synthetic data scene are received. The values correspond to asset-variation parameters or scene-variation parameters. Based on the values, the synthetic data scene is generated using the first synthetic data asset and the second synthetic data asset.

Various embodiments, methods and systems for implementing a distributed computing system scene assembly engine are provided. Initially, a selection of a first synthetic data asset and a selection of a second synthetic data asset are received from a distributed synthetic data as a service (SDaaS) integrated development environment (IDE). A synthetic data asset is associated with asset-variation parameters and scene-variation parameters, the asset-variation parameters and scene-variation parameters are programmable for machine-learning. Values for generating a synthetic data scene are received. The values correspond to asset-variation parameters or scene-variation parameters. Based on the values, the synthetic data scene is generated using the first synthetic data asset and the second synthetic data asset.

Techniques are described herein that are capable of integrating an IDE with game engines. States of the game engines are identified. Each state indicates whether the IDE enables a game developer to interact with the respective game engine and/or game(s) created by the respective game engine. A subset of the game engines is caused to be displayed to the game developer based at least in part on the IDE enabling the game developer to interact with each game engine in the subset and/or game(s) created by the respective game engine. A selection indicator, which indicates that a game engine is selected from the game engines in the subset, is received. An integration infrastructure, including a game engine-agnostic messaging protocol and game engine-agnostic messages, is provided. At least a portion of game code and/or test unit(s) are run and/or debugged using the IDE in a context of the selected game engine.

Distinguishing pattern differences from non-pattern differences. A set of differences is identified. The set comprises a plurality of differences between first and second versions of a document. A pattern is identified. The pattern explains a transformation from a first string in the first version of the document to a second string in the second version of the document. A subset of differences are identified. The subset comprises a plurality of differences, from among the set, which match the pattern. While presenting a user interface that visually highlights differences between the first and second versions of the document, a first visual treatment is applied to a first difference, based on the first difference being included in the subset. A second visual treatment is also applied to a second difference, based on the second difference being excluded from the subset. The second visual treatment is different than the first visual treatment.

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

A method of generating a user interface for presentation to a user. The method comprises executing a first application computer program to provide a user interface, executing agent computer program code to interrogate and modify said user interface during execution of said first application computer program, and presenting said modified user interface. The first application computer program may be run on a server, while the modified user interface may be presented to a user at a client connected to said server.

A method of generating a user interface for presentation to a user. The method comprises executing a first application computer program to provide a user interface, executing agent computer program code to interrogate and modify said user interface during execution of said first application computer program, and presenting said modified user interface. The first application computer program may be run on a server, while the modified user interface may be presented to a user at a client connected to said server.

A character recommendation method and apparatus, a computer device, and a storage medium are disclosed. The method includes: converting code inputted in a code input interface into a syntax tree, the syntax tree including a plurality of nodes, a hierarchical relationship between the plurality of nodes, and location intervals of the plurality of nodes; determining, according to a cursor location in the code input interface and the syntax tree, at least one reference node corresponding to the cursor location in the syntax tree; parsing the at least one reference node, and determining a to-be-recommended target character according to a parsing result; and recommending the target character in the code input interface.

A character recommendation method and apparatus, a computer device, and a storage medium are disclosed. The method includes: converting code inputted in a code input interface into a syntax tree, the syntax tree including a plurality of nodes, a hierarchical relationship between the plurality of nodes, and location intervals of the plurality of nodes; determining, according to a cursor location in the code input interface and the syntax tree, at least one reference node corresponding to the cursor location in the syntax tree; parsing the at least one reference node, and determining a to-be-recommended target character according to a parsing result; and recommending the target character in the code input interface.