Abstract: An image of a virtualization software and firmware in a plurality of hosts are upgraded by performing the steps of: in response to a user input, generating a software specification that specifies a desired state of the virtualization software and a desired state of the firmware; based on the software specification, preparing a desired image of the virtualization software that includes software components of a desired version of the firmware; and coordinating an upgrade to the desired image of the virtualization software and the desired version of the firmware in the hosts. The upgrade includes an upgrade in each of the hosts to the desired version of the firmware, and an upgrade in each of the hosts to the desired image of the virtualization software that includes the software components of the desired version of the base image and the software components of the desired version of the firmware.

Abstract: The disclosure provides an approach for a non-disruptive system upgrade. Embodiments include installing an upgraded version of an operating system (OS) on a computing system while a current version of the OS continues to run. Embodiments include entering a maintenance mode on the computing system, including preventing the addition of new applications and modifying the handling of storage operations on the computing system for the duration of the maintenance mode. Embodiments include, during the maintenance mode, configuring the upgraded version of the OS. Embodiments include, after configuring the upgraded version of the OS, suspending a subset of applications running on the computing system, transferring control over resources of the computing system to the upgraded version of the OS, and resuming the subset of the applications running on the computing system. Embodiments include exiting the maintenance mode on the computing system.

Abstract: A device may detect a trigger to perform a benchmarking task. The benchmarking task may include a first benchmarking of a first resource utilization associated with one or more tasks completed via an automated procedure. The benchmarking task may include a second benchmarking of a second resource utilization associated with the one or more tasks completed via a manual procedure. The device may determine project data relating to a project platform based on detecting the trigger to perform the benchmarking task. The device may process the project data relating to the project platform to benchmark the project. The device may generate a recommendation relating to completion of the one or more tasks using the automated procedure or the manual procedure. The device may communicate with one or more other devices to perform a response action based on the recommendation.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: The disclosure provides an approach for a non-disruptive system upgrade. Embodiments include installing an upgraded version of an operating system (OS) on a computing system while a current version of the OS continues to run. Embodiments include entering a maintenance mode on the computing system, including preventing the addition of new applications and modifying the handling of storage operations on the computing system for the duration of the maintenance mode. Embodiments include, during the maintenance mode, configuring the upgraded version of the OS. Embodiments include, after configuring the upgraded version of the OS, suspending a subset of applications running on the computing system, transferring control over resources of the computing system to the upgraded version of the OS, and resuming the subset of the applications running on the computing system. Embodiments include exiting the maintenance mode on the computing system.

Abstract: The disclosure provides an approach for a non-disruptive system upgrade. Embodiments include installing an upgraded version of an operating system (OS) on a computing system while a current version of the OS continues to run. Embodiments include entering a maintenance mode on the computing system, including preventing the addition of new applications and modifying the handling of storage operations on the computing system for the duration of the maintenance mode. Embodiments include, during the maintenance mode, configuring the upgraded version of the OS. Embodiments include, after configuring the upgraded version of the OS, suspending a subset of applications running on the computing system, transferring control over resources of the computing system to the upgraded version of the OS, and resuming the subset of the applications running on the computing system. Embodiments include exiting the maintenance mode on the computing system.

Abstract: The disclosure provides an approach for a non-disruptive system upgrade. Embodiments include installing an upgraded version of an operating system (OS) on a computing system while a current version of the OS continues to run. Embodiments include entering a maintenance mode on the computing system, including preventing the addition of new applications and modifying the handling of storage operations on the computing system for the duration of the maintenance mode. Embodiments include, during the maintenance mode, configuring the upgraded version of the OS. Embodiments include, after configuring the upgraded version of the OS, suspending a subset of applications running on the computing system, transferring control over resources of the computing system to the upgraded version of the OS, and resuming the subset of the applications running on the computing system. Embodiments include exiting the maintenance mode on the computing system.

Abstract: An image of a virtualization software and firmware in a plurality of hosts are upgraded by performing the steps of: in response to a user input, generating a software specification that specifies a desired state of the virtualization software and a desired state of the firmware; based on the software specification, preparing a desired image of the virtualization software that includes software components of a desired version of the firmware; and coordinating an upgrade to the desired image of the virtualization software and the desired version of the firmware in the hosts. The upgrade includes an upgrade in each of the hosts to the desired version of the firmware, and an upgrade in each of the hosts to the desired image of the virtualization software that includes the software components of the desired version of the base image and the software components of the desired version of the firmware.

Abstract: The disclosed system and method for software testing provide a strategy for testing new software functionalities in a sequence that detects defects according to a testing strategy designed to accomplish a predetermined objective. Providing a pre-testing stage generates test execution results for test cases designed to find defects related to new functionalities. Using machine learning clustering techniques to identify which test cases are similar to one another facilitates organizing test cases in a sequence appropriate for accomplishing a predetermined objective.

Abstract: In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Abstract: An image of a virtualization software and firmware in a plurality of hosts are upgraded by performing the steps of: in response to a user input, generating a software specification that specifies a desired state of the virtualization software and a desired state of the firmware; based on the software specification, preparing a desired image of the virtualization software that includes software components of a desired version of the firmware; and from a virtual machine management server that is programmed to deploy virtual machines in the hosts, coordinating an upgrade to the desired image of the virtualization software and the desired version of the firmware in the hosts by (1) instructing a hardware support manager to upgrade the hosts to the desired version of the firmware, and (2) instructing each of the hosts to upgrade to the desired image of the virtualization software.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: An image of a virtualization software and firmware in a plurality of hosts are upgraded by performing the steps of: in response to a user input, generating a software specification that specifies a desired state of the virtualization software and a desired state of the firmware; based on the software specification, preparing a desired image of the virtualization software that includes software components of a desired version of the firmware; and from a virtual machine management server that is programmed to deploy virtual machines in the hosts, coordinating an upgrade to the desired image of the virtualization software and the desired version of the firmware in the hosts by (1) instructing a hardware support manager to upgrade the hosts to the desired version of the firmware, and (2) instructing each of the hosts to upgrade to the desired image of the virtualization software.

Abstract: The disclosed system and method for software testing provide a strategy for testing new software functionalities in a sequence that detects defects according to a testing strategy designed to accomplish a predetermined objective. Providing a pre-testing stage generates test execution results for test cases designed to find defects related to new functionalities. Using machine learning clustering techniques to identify which test cases are similar to one another facilitates organizing test cases in a sequence appropriate for accomplishing a predetermined objective.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: A data analytics platform may determine whether a machine learning model is a regression model. The data analytics platform may perform, based on determining that the machine learning model is a regression model, a regression prescription method including acquiring a predicted value of a performance indicator determined by the machine learning model processing data associated with a plurality of features and the performance indicator, acquiring a target value of the performance indicator, determining a rate of change of the performance indicator with respect to each feature to generate first results, determining, based on the regression model and for each feature, a rate of change of each feature with respect to other features to generate second results, and determining, for each feature and based on the predicted value, the target value, the first results, and the second results, a change in each feature to achieve the target value.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: A project management platform may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project. The project management platform may receive new project data identifying project information associated with a new project. The project management platform may perform natural language processing on the new project data to convert the new project data to processed new project data. The project management platform may receive resource data identifying resource availability for the new project. The project management platform may process, using the trained machine learning model, the processed new project data and the resource data to determine release information for the new project, wherein the release information includes a release schedule for the new project. The project management platform may perform, according to the release schedule, an action associated with the release information.

Abstract: A device may determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

Abstract: A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.