Abstract: Load balancing and distributing issue requests for resolution by assignable resources is described. A multimodal model determines attributes of an input issue request. A lower dimensional representation of the input issue request is generated based on the attributes; and compared to other lower dimensional representations of other issue requests to identify prior similar issue requests. The multi modal model outputs indications of similarity strength for the prior similar issue requests, and the resources assigned to resolve the prior similar issue requests. An expertise level for each assignable resource is determined based on the similarity strength and the previously assigned resources. A routing metric for each assignable resource is determined based on a combination of the expertise level and an availability of each assignable resource; and the input issue request is routed to an assignable resource in the assignable resources based on the routing metric.

Abstract: Systems and methods are provided for implementing component-based scheduling and release readiness determination. A computing system automatically registers new hardware and virtual machines, establishing dependencies to avoid invalid scenarios. The computing system dynamically generates testing plans by combining reusable components (e.g., shared components) and continuously updates coverage based on existing configurations. In some examples, artificial intelligence (“AI”) tools recommend coverage and scheduling plans to enhance efficiency. Additionally, the computing system utilizes an actionable test scheduling and diagnostics tool, which is based on a product validation standard, that identifies critical configurations, assesses software release readiness, and estimates realistic shipping targets. The actionable test scheduling and diagnostics tool assists users in meeting their software product shipping goals while ensuring reliable product performance and minimizing the validation workload.

Abstract: The techniques described herein provide a risk assessment framework that enhances the functionality of software deployment systems in cloud-based platforms. Generally described, the present techniques evaluate and consolidate various risk scores to classify a given computing cluster within a software deployment strategy. In various examples, a deployment system collects node-level feature data from the computing cluster to generate a dataset to train a prediction model to calculate constituent risk scores. In another aspect, the deployment system aggregates constituent risk scores to determine an overall risk of software failure. Likewise, the deployment system considers diverse criteria such as virtual machine size and virtual machine density to determine an overall impact of software deployment failure. The deployment system then calculates a composite risk score for the computing cluster as a function of the risk of software deployment failure and the impact of software deployment failure.

Abstract: Some embodiments of the invention provide an interface between programmed instructions (e.g., an operating system) and a miniport driver configured to communicate with radio hardware on a computer. The interface may include components operable to invoke various wireless connectivity-related functionality implemented by the radio hardware and/or miniport driver. The functionality may, for example, include a capability whereby the computer may maintain simultaneous connections on a plurality of wireless networks using a single radio, and/or a capability whereby the computer may function as an access point for a wireless network.