A system, method, and computer-readable medium are disclosed for performing a customer operating system installation operation. The customer operating system installation operation includes performing a customer operating system installation operation onto an information handling system, comprising: performing a customer operating system installation operation; and, performing a UEFI boot entry operation, the UEFI boot entry operation accessing a UEFI boot entry when performing the customer operating system installation operation, the UEFI boot entry operation providing a communication abstraction between a manufacturing operating system and the customer operating system.

An example embodiment involves persistent storage defining a first configuration item representing an application deployed within a network, a second configuration item representing a software program that is deployable within the network, and a relationship between the first configuration item and the second configuration item. One or more processors may be configured to: (i) receive an indication that a change has been applied to the application or has been arranged to be applied to the application; (ii) identify the relationship between the first configuration item and the second configuration item; (iii) based on the relationship between the first configuration item and the second configuration item, determine that the change can affect operation of the software program; and (iv) in response to determining that the change can affect operation of the software program, provide a notification of the change to an agent associated with the software program.

There is disclosed herein examples of systems and procedures for performing software updates for vehicles. The vehicles may be scheduled for the software updates based on information related to the vehicles. Update systems may determine when the vehicles have entered service ranges of the update systems for the scheduled software updates and may initiate the software updates in response to determining that the vehicles have entered the service ranges. Progress of the software updates may be monitored and displayed on a dashboard system overseeing the software updates of the vehicles.

The present disclosure relates to system(s) and method(s) for verifying data loading requirements of an avionics unit. The system receives a request for data loading. The request comprises file data, and data loading requirements associated with the avionics unit. Further, the system obtains target file from a repository based on an analysis of the request. The system further generates valid data set and invalid data set in the target file based on an analysis of the data loading requirements. Upon generation, the system verifies predefined data loading requirements of the avionics unit using the invalid data set from the target file.

Systems and methods are provided for determining, at an operator executed on a server that is separate from an application, whether to perform an update of the application. The operator may perform an upgrade precheck when it is determined that the update to the application is to be performed. The precheck may include determining whether a database migration is to be performed as part of the update to the application, and receiving an update mode and an update type to determine the upgrade to the application. The operator may provide to the application via an application program interface (API), one or more application shutdown configuration parameters for the update based on the received update mode and update type of the upgrade precheck. The operator may deploy the update to the application based on the determined update mode and update type.

The present disclosure includes methods, devices and systems for establishing a connection between a medical device and a remote computing device, receiving an upgrade command at the medical device, storing a current version of persistent data and a current version of executable code in a first storage area of the medical device, transmitting at least the current version of the persistent data to the remote computing device, receiving a second format of the current version of the persistent data and an upgraded version of executable code at the medical device, storing the second format of the current version of the persistent data and the upgraded version of the executable code in a second storage area of the medical device, and executing the upgraded version of the executable code with the second format of the current version of the persistent data.

In some examples, a computing device comprises a processing resource and a memory resource storing instructions to cause the processing resource to detect, by a basic input/output system (BIOS) of the computing device, firmware corruption in a firmware component of the computing device, generate a recovery agent based on the detected firmware corruption of the firmware component, determine a location of a back-up image of the firmware component based on the generated recovery agent, determine recovery sequence based on the determination of the location of the back-up image of the firmware component; and recover the firmware of the firmware component by executing the determined recovery sequence.

Disclosed herein is a system for improving the user experience in the face of a regression by returning resources that offer a service to a “last known good” upgrade. In other words, the state of the resources is reconfigured to scale back from recent upgrade(s), the deployments of which likely caused the regression, to a previous upgrade that is known to have little or no effect on the user experience. To identify a problem, the system collects performance data from different resource units that make up a cloud-based platform. The performance data is collected for each upgrade event in a sequence of upgrade events that are currently deployed or being deployed. The system continually tracks and analyzes qualification data collected for each of the deployed upgrade events. The system can tag an upgrade event as the last known good upgrade event when the collected qualification data satisfies predefined qualifications.

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.

Methods, systems, apparatuses, and computer program products are described. A multi-tenant database system may store a set of data logs indicating performance data for multiple tenants of the system. The system may calculate one or more aggregate performance metrics based on performance data for a tenant stored in the logs, where a performance metric of the one or more aggregate performance metrics may be based on design time data for the tenant, runtime data for the tenant, or both. The system may compare the one or more aggregate performance metrics to one or more performance thresholds defined for multiple tenants and may generate scalability scores corresponding to the one or more aggregate performance metrics for the tenant. The system may send, for display at a user interface of a user device operated by a user associated with the tenant, an indication of the generated scalability scores.