Microsoft Technology Licensing, LLC

James M Pinkerton

Jordi Mola

Jackson Michael Davis

Andrew R Sterland

Del Myers

Leslie Yvette Richardson

Thomas Lai

Patrick Nelson

Terry Farrell

Juan Carlos Arevalo Baeza

Kenneth W Sykes

David Ahs

Jordi Mola Marti

Deborah Chen

Peter A Thompson

Travis Austin Wright

Jan Anders Nelson

Mark Marron

Baelson B Duque

Brett Eddy

Dale Koetke

James Anders Nelson

Andrew Joseph Luhrs

Timothy Gardner Misiak

Patrick Lothian Nelson

Automatic identification of execution behavior(s) of software. This automatic identification is based on analysis of historical execution records using machine learning to identify a particular pattern that corresponds to an execution behavior. In order to automatically identify an execution behavior present within particular software, an execution record of that particular software is accessed. The execution record includes an execution trace that reproducibly represents the execution of the software within a particular execution environment, such that the execution record is usable to rerun the execution of the software precisely as the software previously run. Based on finding the particular pattern within the execution record, the computing system automatically identifies that the execution behavior is present within the software.

Automatic software behavior identification using execution record

Based on replay of a thread, one implementation observes an influx of a value of a memory cell comprising an interaction between the thread and the value of the memory cell at an execution time point in the replaying, and determines whether the value of the memory cell observed from the influx is inconsistent with a prior value of the memory cell as known by the thread at the execution time point. If so, this implementation initiates an indication of a data inconsistency. Based on replay of a plurality of threads, another implementation identifies a memory cell that was accessed by a first thread while a thread synchronization mechanism was active on the first thread. Then, if there was another access to the memory cell by a second thread without use of the thread synchronization mechanism, this implementation initiates an indication of a potential data contention.

Identifying data inconsistencies and data contention based on historic debugging traces

Identifying and reporting potential data inconsistencies and/or potential data contentions based on historic debugging traces. Based on replay of a thread, some implementations observe an influx of a value to a memory cell, and determine whether the value of the memory cell observed from the influx is inconsistent with a prior value of the memory cell as known by the thread. If so, these implementations can initiate an indication of a data inconsistency. Based on replay of a plurality of threads, other implementations identify a memory cell that was accessed by a first thread while a thread synchronization mechanism was active on the first thread. Then, if there was another access to the memory cell by a second thread without use of the thread synchronization mechanism, these implementations might initiate an indication of a potential data contention.

This disclosure relates to identifying and presenting differences between a plurality of recorded executions of an executable entity. One or more models are created over the plurality of recorded prior executions of at least a portion of an executable entity. These models include at least one of (i) a control flow model, or (ii) a data model. An anomalous model data point is identified within these models, and a first location in at least one of the plurality of recorded executions that corresponds to the anomalous model data point is identified. A second location in the at least one of the plurality of recorded executions is also identified. This second location is causal to the anomalous model data point at the first location. The identity of the first and/or second locations in the least one of the plurality of recorded executions is presented.

Automatically identifying and highlighting differences between historic traces

The automatic identification of execution behavior(s) of software. This automatic identification is based on a historical analysis of execution records to identify a particular pattern that represents an execution behavior. In order to automatically identify an execution behavior present within particular software, an execution record (or perhaps multiple execution records) representing the execution of that particular software may be accessed. Based on finding the particular pattern within the execution record (or one, some, or all of the multiple execution records) representing the execution of that particular software, the computing system may automatically identify that the execution behavior is present within the software. This may dramatically assist in modifying that execution behavior.

Techniques are provided to use historic execution state information to visualize tracepoint data. For example, historic execution state information corresponding to an application's previous execution is accessed. This historic execution state information was collected while the application was executing. After correlating the historic execution state information to the application's code, a tracepoint is associated with a portion of the code. Consequently, when the code is replayed based on the historic execution state information, the tracepoint causes a behavior of that code portion to be logged while the code is replayed uninterrupted. The code is then actually replayed based on the historic execution state information. During the replay, the tracepoint causes the behavior of the code portion to be logged. The logged behavior is visualized on a user interface.

Using historic execution data to visualize tracepoints

Electronic files for a localization project may be created and/or managed. The electronic files may be based on a data structure that defines the format and content of the electronic files and may include a first data structure portion that may contain data representing a plurality of references to localization project files. The data structure may includes a second data structure portion that may be associated with the first data structure portion that may contain data that may represent a plurality of references to standard files.

Creation and management of electronic files for a localization project

Creation and management of electronic files for a localization project. The electronic files are based on a data structure that defines the format and content of the electronic files and includes a first data structure portion that contains data that represents a plurality of references to localization project files. Moreover, the data structure includes a second data structure portion that is associated with the first data structure portion that contains data that represents a plurality of references to standard files.

Creation and management of electronic files for localization project

Systems and methods for playbook automation are described. In one aspect, a user selects operational knowledge associated with an application. The user associates one or more playbook-based tasks, playbook-based views, or playbook-based links with the operational knowledge. The playbook-based tasks, playbook-based views, and playbook-based links are for diagnosing, resolving, and/or verifying a problem associated with the application. The systems and methods generate an integrated management pack from the associated operational knowledge and at least one of the one or more playbook-based tasks, playbook-based views, or playbook-based links.

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Kirkland, WA, United States of America

James M Pinkerton