The present disclosure provides a light-emitting substrate. The light-emitting substrate includes a backboard, a light-emitting layer and a plurality of first optical bodies. The light-emitting layer is located on a side of the backboard; the light-emitting layer includes a plurality of light-emitting units, and the plurality of light-emitting units are arranged in an array. Each first optical body includes a first optical portion and a second optical portion; a gap between two adjacent light-emitting units is filled with the first optical portion, and the second optical portion is located on a side of the light-emitting layer away from the backboard, and is connected to the first optical portion. The second optical portion includes a first surface extending outwards from an edge of the first optical portion.

This disclosure describes techniques that include validation or other assessments of digital systems, such as machine learning models and other statistical models. In one example, this disclosure describes a method that includes receiving, by a validation computing system and from a development system, a request to perform a test on a model configured to execute on the development system; outputting, by the validation computing system to the development system and in response to the request, an instruction; enabling the development system to process the instruction; receiving, by the validation computing system, test response data; evaluating, by the validation system, the test response data.

A method includes monitoring an interaction of a user with a metaverse session. A notification is sent to a first developer that the user is facing an issue while interacting with the metaverse session. The first developer is allowed access to the metaverse session at a location of the issue using a first developer avatar. One or more error flags are generated within a field of view of the first developer avatar in the metaverse session. Each error flag includes a respective error code. In response to determining that a first error code of a first error flag matches a first stored error code, a first solution is determined. The first solution includes a first stored solution corresponding to the first stored error code. In response to determining that the first solution solves the issue of the user, the first solution is deployed to the metaverse session.

A system and method for CI/CT/CD, which is continuous integration/continuous testing/continuous delivery, in which testing is fully integrated to the needs of rapid code development and delivery. Such a system and method may also determine a relative importance of a selected test in a plurality of tests, such that the system and method may comprise a computational device for receiving one or more characteristics relating to an importance of the code, an importance of each of the plurality of tests, or both; and for determining the relative importance of the selected test according to said characteristics.

Systems and methods for testing one or more code merges is disclosed. The system includes a processor coupled to a memory. The processor is configured to receive a request for merging one or more design codes corresponding to one or more designs of a build card with one or more functional codes for generating a merged output. The processor is further configured to capture one or more output screenshots of the merged output. In addition, the processor is configured to evaluate the merged output by comparing the one or more output screenshots with one or more input screenshots that include the one or more designs prior to the generation of the merged output.

Techniques, which may be embodied herein as systems, computing devices, methods, algorithms, software, code, computer readable media, or the like, are described herein for comparing a set of metrics generated during a simulated user interaction with a website to metrics generated by observing real user interactions with the website. Simulated user interactions with a website can be used to diagnose a website's performance issues, but it can be difficult to determine whether the simulated interactions reflect the experience of real users. In addition, the simulated user interactions can be challenging to contextualize because the number of observed real user interactions may significantly outnumber the simulated interactions. A graphical user interface can help with the interpretation of these website interactions by using the real user interactions to properly contextualize the simulated results.

A software development infrastructure can enable user developers to select remote hardware devices at a remote datacenter to develop and test software programs, such as web or mobile applications. The developer can remotely install an application on a selected remote device and observe a mirrored display of the remote device on a browser local to the developer. The software development infrastructure can enable the developer to test offline mode workflows of the application by blocking network traffic to and from the application but allowing network transmission to and from a streaming application installed on the remote device.

A computer system for testing a user interface includes a memory circuit and a processor circuit configured to execute instructions including obtaining a state of the user interface. The instructions include setting a current position to a specified location within the user interface. The instructions include executing user interface tests to generate multiple paths. The instructions include determining a shortest path toward a goal location by identifying a path having a minimum distance that satisfies criteria. A distance for the path is based on two-dimensional distances between pairs of consecutive positions that include the current position as well as positions, along the particular path, of user interface elements requiring interaction to satisfy the criteria. The instructions including generating a preferred path based on preferred path information indicative of a specified path toward the goal location, comparing the determined shortest path to the generated preferred path, and outputting an analysis result.

In some implementations, a regression prediction platform may obtain one or more feature sets related to an impending code change, wherein the one or more feature sets may include one or more features related to historical code quality for a developer associated with the impending code change or a quality of a development session associated with the impending code change. The regression prediction platform may provide the one or more feature sets to a machine learning model trained to predict a risk associated with deploying the impending code change based on a probability that deploying the impending code change will cause breakage after deployment and/or a probability that the impending code change will cause support issues after deployment. The regression prediction platform may generate one or more recommended actions related to the impending code change based on the risk associated with deploying the impending code change.

Systems, methods, and devices are provided to intelligently interrogate and tag a codebase. A behavioral model of the codebase is accessed that represents a run-time behavior of the codebase. The behavioral model and codebase are interrogated to identify locations for placing bidirectional tags within the behavioral model and the codebase. The bidirectional tags include links for connecting portions of the behavioral model to corresponding lines of code within the codebase. Selection of a bidirectional tag of the behavioral model causes focus of a user interface to change from a view of a visual depiction of the behavioral model to a view of a corresponding tag in the codebase. Selection of a bidirectional tag of the codebase causes focus of the user interface to change from a view of the codebase to a view of a corresponding tag in the visual depiction of the behavioral model.