A method for software code integration from media files includes comparing portions of development code in an integrated development environment to a plurality of classified portions of a plurality of media files. The method also includes identifying a first classified portion of a first media file from the plurality of media files for integration into the development code, where the first classified portion includes a first snippet of code associated with a first function. Responsive to receiving a user confirmation, the method also includes extracting the first snippet of code associated with the first function. The method also includes integrating the first snippet of code associated with the first function into the development code in the integrated development environment

A method for software code integration from media files includes comparing portions of development code in an integrated development environment to a plurality of classified portions of a plurality of media files. The method also includes identifying a first classified portion of a first media file from the plurality of media files for integration into the development code, where the first classified portion includes a first snippet of code associated with a first function. Responsive to receiving a user confirmation, the method also includes extracting the first snippet of code associated with the first function. The method also includes integrating the first snippet of code associated with the first function into the development code in the integrated development environment

A source code correction assistance apparatus is configured to include a storage device that stores an updated source code, and an arithmetic operational device that generates, as an evaluation code template of the updated source code, a template including a conditional branch sentence related to each case of success or failure of an input condition, notifies an evaluator terminal of a request to create an evaluation code based on the template, controls an access to the updated source code by the evaluator, receives editing by the evaluator on the conditional branch sentence in the template, generates a list of input values for executing all control paths of the evaluation code after the editing, and generates an evaluation code driver that automatically executes the evaluation code by inputting the input value.

A source code correction assistance apparatus is configured to include a storage device that stores an updated source code, and an arithmetic operational device that generates, as an evaluation code template of the updated source code, a template including a conditional branch sentence related to each case of success or failure of an input condition, notifies an evaluator terminal of a request to create an evaluation code based on the template, controls an access to the updated source code by the evaluator, receives editing by the evaluator on the conditional branch sentence in the template, generates a list of input values for executing all control paths of the evaluation code after the editing, and generates an evaluation code driver that automatically executes the evaluation code by inputting the input value.

During a software development lifecycle of a software application, application code is modified and multiple versions are built and packaged to be installed on different computing systems, such as on a software development computing system, a software testing computing systems, and/or production or end-user computing systems. A runtime error optimization engine analyzes, using a first artificial intelligence model, a build package to predict whether it may encounter runtime errors causing an installation to fail. When an error is identified, a runtime error orchestration engine may utilize a second artificial intelligence model to identify a solution, where the runtime error orchestration engine rebuilds the build package based on an identified solution and initiates installation via a deployment pipeline.

During a software development lifecycle of a software application, application code is modified and multiple versions are built and packaged to be installed on different computing systems, such as on a software development computing system, a software testing computing systems, and/or production or end-user computing systems. A runtime error optimization engine analyzes, using a first artificial intelligence model, a build package to predict whether it may encounter runtime errors causing an installation to fail. When an error is identified, a runtime error orchestration engine may utilize a second artificial intelligence model to identify a solution, where the runtime error orchestration engine rebuilds the build package based on an identified solution and initiates installation via a deployment pipeline.

Disclosed herein is technology to capture and restore a state of a development environment. An example method may include: determining, by a processing device, a state of a first development environment, wherein the first development environment displays content of a set of files that correspond to a program modification; storing state data that represents the state of the first development environment, wherein the state data identifies the files in the set; receiving a request to update a second development environment; and updating, using the state data, a state of the second development environment, wherein the updated state of the second development environment displays the content of the set of files corresponding to the program modification.

A code generation system uses a non-terminal expansion model and a non-terminal selector model to generate a code sketch to complete a partially-formed source code snippet. The non-terminal expansion model is a neural transformer model trained on a supervised dataset through reinforcement learning to learn to predict the production rule to expand for a given non-terminal symbol. The non-terminal selector model is trained through reinforcement learning to predict the non-terminal symbol to expand given a partial-code state. The models are used in a two-step beam search to generate the top candidate code sketches, where a candidate code sketch may contain a hole that represents an unexpanded non-terminal symbol.

A code generation system uses a non-terminal expansion model and a non-terminal selector model to generate a code sketch to complete a partially-formed source code snippet. The non-terminal expansion model is a neural transformer model trained on a supervised dataset through reinforcement learning to learn to predict the production rule to expand for a given non-terminal symbol. The non-terminal selector model is trained through reinforcement learning to predict the non-terminal symbol to expand given a partial-code state. The models are used in a two-step beam search to generate the top candidate code sketches, where a candidate code sketch may contain a hole that represents an unexpanded non-terminal symbol.

A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.