Microsoft Technology Licensing, LLC

Zhongyuan Li

Lei Kou

Wei Huang

Tianzhuang Dou

Christian A Hernandez Sanchez

Brian A Pokrifka

Rajeswari Rajagopalan

William P Donkervoet

Joshua B Bruno

Carla M Difranco

Systems and methods for selectively allowing or blocking operating system upgrades based on probabilities that the upgraded operating system may result in a fault condition. In an example, the technology may perform a method that includes receiving usage data from a client device to evaluate providing an operating system upgrade for the client device; accessing a root-cause factor that is likely to lead to a fault condition with the operating system upgrade; based on the usage data, identifying a fault probability for the root-cause factor; comparing the fault probability to a fault threshold for the root-cause factor; based on the comparison of the fault probability and the fault threshold, performing one of: blocking the operating system upgrade from being installed on the client device; or providing the operating system upgrade for installation on the client device.

Controlling operating system upgrades based on usage data of client device

Automatic detection of software program regression points enables developers to more quickly and accurately diagnose and solve issues in changes to software. Traces of instructions are collected from before and after a change to a program or settings thereto on a computing device and are compared to identify differences between the two traces. Areas of differences are ranked and returned to identify the point(s) of regression in the traces to alert a user of programs of behaviors to avoid and/or a developer a point to focus efforts on to patch the responsible program or update other programs around the responsible application.

Deviation finder

A system generates a command to cause a computing machine to collect information for an issue that occurred with an application. The command is stored in a command list and associated with a computing machine identifier for the computing machine. The system receives a query from the computing machine for the command list. The query includes the computing machine identifier and the system locates the command in the command list based on the command being associated with the computing machine identifier. The system then sends the command to the computing machine where the command dynamically causes the computing machine to collect the information for the issue with the application.

Dynamic command generation for automated control of computing machines

Enhanced longest common subsequence techniques are provided to improve efficiency and accuracy of any process that uses long sequences. Two superscale data sequences are received. Each data sequence is divided into a set of segments based on one or more heuristics or user input. From the sets of segments, pairs of segments are selected and processed using any longest common subsequence algorithm to determine the longest common subsequence for each pair of segments. The results of the processing are merged to determine the enhanced longest subsequence which is the longest common subsequence for the received superscale data sequences. Dividing the superscale data sequences into segments allows for parallel processing of each segment pair, which results in a faster completion time than prior art methods for determining longest common subsequences.

Determining enhanced longest common subsequences

A static multi-entry point function with more than one entry point is provided. The function allows callers to enter the function at different entry points using an offset. Each entry point of the function is associated with a different offset, and includes instructions that identify data that is associated with the entry point. Each entry point further includes an instruction that jumps to a common prologue of the function. The common prologue loads the identified data into local variables. The function also includes a functional component that performs different actions depending on the data in the local variables. The function includes a default entry point that is used when the function is called without any offset, so that the function appears to behave like a normal function to scanners.

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Zhongyuan Li