Microsoft Technology Licensing, LLC

David Gregory Grant

Silviu C Calinoiu

William P Campbell

Anthony J Lorelli

Pavan Kasturi

Jack Wilson Stokes, Iii

Lei Zhang

Cong Chen

Vamshidhar Radha Kommineni

Ryan S Kivett

Kevin Bartz

Gretchen L Loihle

Feng Liang

Weijun Guo

Navid Jalali Heravi

Chittibabu Pacharu

Paul Martin Messing

Xiaowen Wang

Kevin Alan Erickson

Ivan Michael Berg

Shenghao Li

Sk Kajal Arefin Imon

Michael D Moshofsky

Some disclosed embodiments are directed to methods and systems for performing pair-wise delta compression. For example, systems obtain a set of files to be compressed into a single compressed file. The system identifies different attributes related to the set of files. For each file in the set of files, the system predicts an optimized set of candidate compression files and calculates a delta between each file in the optimized set and the target file corresponding to the optimized set. After identifying the smallest delta, the system compresses the selected pair of files associated with the smallest delta in order to generate the single compressed file for the set of files.

Systems and methods for pair-wise delta compression

Techniques for identifying a stack frame responsible for resource usage are described. For instance, techniques described herein enable a particular process and a particular stack frame and/or set of stack frames of the process that are high resource consumers to be identified. According to various implementations, resource usage of a process is observed and recorded over a period of time. A data structure is generated that characterizes the resource usage of the process over the sampling period. The data structure be evaluated to identify a stack frame that is responsible for excess resource consumption. In at least some implementations, a remedial procedure can be performed to attempt to reduce the process's resource usage.

Identifying a stack frame responsible for resource usage

Techniques for determining similarity between error reports received by an error reporting service. An error report may be compared to other previously-received error reports to determine similarity and facilitate diagnosing and resolving an error that generated the error report. In some implementations, the similarity may be determined by comparing frames included in a callstack of an error report to frames included in callstacks in other error reports to determine an edit distance between the callstacks, which may be based on the number and type of frame differences between callstacks. Each type of change may be weighted differently when determining the edit distance. Additionally or alternatively, the comparison may be performed by comparing a type of error, process names, and/or exception codes for the errors contained in the error reports. The similarity may be expressed as a probability that two error reports were generated as a result of a same error.

Similarity detection for error reports

A mitigation enablement module for a computer that improves application reliability. When performing memory management operations, the mitigation enablement module and associated memory manager selectively use mitigations that are intended to prevent an application bug from cause an application error. The memory manager may selectively apply mitigations for each of one or more applications based on the likelihood that such mitigations are successful at preventing bugs from causing application errors. The likelihood is determined from historical information on whether the mitigations, when applied, prevented bugs from causing memory operations that could cause application errors. This historical information can be gathered on a single computer over multiple invocations of the application or may be aggregated from multiple computers, each invoking the application. The determined likelihood may then be used to determine whether or for how long to apply the mitigation actions for memory operations requested by the application.

Memory management techniques selectively using mitigations to reduce errors

Techniques for performing memory management to mitigate memory errors. In accordance with the principles described herein, a memory management module may be implemented that acts in different modes of operation for each of one or more software modules that are instances of applications. In one mode of operation, memory operations may be performed in accordance with one or more mitigation actions, and in another mode of operation, the memory management module performs memory operations as requested, without performing mitigation actions. A memory management module may maintain a record in a data store associated with the memory management module that may be used to determine whether to enable the mitigations. In some implementations, records maintained by each of a plurality of computing devices may be aggregated at a central server and this aggregated information may be used to adjust the entries on each of the computing devices.

Configuration of memory management techniques selectively using mitigations to reduce errors

Techniques for performing memory management to mitigate memory errors. In accordance with the principles described herein, a memory management module may be implemented that acts in different modes of operation for each of one or more software modules issuing requests for performance of memory operations to the memory management module. In one mode of operation, one or more mitigation actions may be performed by the memory management module in addition to or instead of the memory operation requested by the calling software module, such that the memory operations are performed in accordance with the mitigation actions. These mitigation actions may serve to reduce a likelihood of a memory error negatively affecting the calling software module. In another mode of operation, the memory management module performs memory operations as requested, without performing mitigation actions.

Evaluating effectiveness of memory management techniques selectively using mitigations to reduce errors

A method of analyzing problem data from a computer application is disclosed. The method evaluates a memory dump, identifying call stacks within the memory dump that are related to application failures, creates a hash of the identified call stack and adds the hash to a database. The database may then be evaluated to look for trends in the error data such as whether the same call stack is causing problems.

Analyzing problem signatures

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Duvall, WA, United States of America

David Gregory Grant