- Number
- 12299420
- Published
- 2025-05-13
- Filed
- 2023-03-02
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Cox; Jason Alexander et al.
- CPC
- G06F11/3604; G06F8/71; G06N20/00; G06F11/0793; G06N3/08; G06F11/3688; G06F8/36; G06F8/77
- Verdict
- Set aside software devops/CI tooling, business
- Source
- Google Patents · FreePatentsOnline
Abstract
In some embodiments, a method receives a change to data stored in a repository. An artifact that is generated based on the change to the data failed a validation. The method analyzes the change to the data via a model to generate a set of adjustments. The model is trained to output adjustments for the artifact to generate a set of adjusted artifacts. The method determines an adjusted artifact that is associated with an adjustment in the set of adjustments that passes the validation. The adjusted artifact is output as a validated artifact.
Background
BACKGROUND (1) When developing software code, a user submits changes to the software code to a source code repository. A system processes those changes and produces an artifact. The artifact may be executables, libraries, and/or a byproduct that is produced by the change to the software code. When the user attempts to use or execute the artifact, the artifact may not work. For example, if a character is being animated, the animated character produced by the artifact may not move properly or as the user may desire. The user must then make changes to the software code and re-submit those changes to the source code repository. The process continues until the user can determine a working artifact is produced. (2) The above manual loop requires a large amount of user time. However, the errors that may have occurred may often be the result of similar simple mistakes that many users have made. However, each user may be required to manually inspect the code and make changes to correct the errors individually. Also, the changes may not be an optimal change or the best solution, and determining the changes may take an undesirably long time to figure out.
Claims
1. A method comprising: receiving, by a computing device, a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed; analyzing, by the computing device, the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data; generating, by the computing device, a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity; determining, by the computing device, an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and outputting, by the computing device, the adjusted artifact entity as a validated artifact entity. ||
15. A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for: receiving a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed; analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data; generating a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity; determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and outputting the adjusted artifact entity as a validated artifact entity. ||
18. An apparatus comprising: one or more computer processors; and a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for: receiving a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed; analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data; generating a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity; determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and outputting the adjusted artifact entity as a validated artifact entity. ||
19. A method comprising: receiving a change to data stored in a repository wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation of an appearance of the artifact entity when executed; analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data; generating a set of adjusted artifact entities based on the set of adjustments being from the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity; determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and outputting the adjusted artifact entity as a validated artifact entity.