Microsoft Technology Licensing, LLC

Jackson Michael Davis

Jordi Mola

Thomas Lai

Del Myers

Patrick Nelson

Andrew R Sterland

Leslie Yvette Richardson

John A Shepard

James M Pinkerton

Juan Carlos Arevalo Baeza

Deborah Chen

Tae Hyung Kim

Henry Gabryjelski

Kenneth W Sykes

Gregg Bernard Miskelly

Gregory Bernard Miskelly

Eric H Feiveson

Steve Carroll

Nelson Mandela Billing

Colin A Thomsen

Lin Xu

Brent Rector

Mark Marron

Eric Hyde Carter

Azeemullah Khan

Stephen J Cook

Saji Abraham

Oleg Izmerly

Nikolaus Lee Karpinsky

Paul Stubbs

Philo N Nordlund

Andrew Brian Hall

Mark Anthony Jelf Downie

Daniel Joshua Taylor

Daniel Demetris George Moth

Esteban Herrera

Noah Falk

Adrian Stevenson Miller

Nikhil Ravindra Joglekar

Wai Hang (“Barry”) Tang

Andrew Joseph Luhrs

Timothy Gardner Misiak

Karl Melder

Zhaoqing Wu

Patrick Fenelon

Maciej Foks

Patrick Lothian Nelson

Embodiments improve software defect diagnosis. Analytic focus is automatically walked back from an initial symptomatic diagnostic context to a previous diagnostic context that is closer to underlying causes. Diagnosis may obtain diagnostic artifacts such as traces or dumps, extract diagnostic context, decompile executables, lookup likely causes based on symptoms, scan logs, and submit diagnostic context to software analysis services. An analysis service may perform static analysis, security testing, symptom-pair lookups, or antipattern scanning, for example, and may include a neural network or other machine learning model, for example. Root causes are culled from analysis results and identified to a software developer. Changes to mitigate the defect's impact are suggested in some cases. Thus, the software developer receives debugging leads without manually navigating through all the tool interfaces or unrelated details of diagnostic contexts. This allows the developer to more efficiently reach a useful diagnosis of defects, even for unfamiliar issues.

Software diagnostic context selection and use

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

A remote procedure call channel for interprocess communication in a managed code environment ensures thread-affinity on both sides of an interprocess communication. Using the channel, calls from a first process to a second process are guaranteed to run on a same thread in a target process. Furthermore, calls from the second process back to the first process will also always execute on the same thread. An interprocess communication manager that allows thread affinity and reentrancy is able to correctly keep track of the logical thread of execution so calls are not blocked in unmanaged hosts. Furthermore, both unmanaged and managed hosts are able to make use of transparent remote call functionality provided by an interprocess communication manager for the managed code environment.

Ensuring thread affinity for interprocess communication in a managed code environment

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

Expressly turning tracing on and off at each juncture between code that a developer wants to have traced and other code may reduce trace file size but adds computational cost. Described technologies support selectively tracing a process's execution, with some extra tracing done beyond the code the developer wanted traced, but with significantly reduced computational cost, by reducing the number of trace enablement and disablement operations. A trace controller uses a tracing disablement distance variable whose values indicate the computational distance from trace disablement. A distance variable modifier automatically moves the distance variable closer to a stop-tracing value as the process executes. The amount of extra tracing is balanced against the reduction in trace enablement/disablement operations by tuning thresholds, based on information about routine size and computational cost. Operation of the trace controller is illustrated by sample APIs, a tracing state diagram, and a side-by-side comparison, among other items.

Selectively tracing portions of computer process execution

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

Performing reverse execution debugging of an application. The method includes identifying a snapshot of application state for an application in an application framework. The method further includes emulating execution of the application, using bytecode, in a forward direction from the snapshot to an end point, causing generation of historical execution state at various points along the emulation of the application. The method further includes collecting the historical execution state. The method further includes using the collected historical execution state, emulating reverse execution of the application by substituting collected historical execution state, when needed, to restore previous state needed as a result of emulation of reverse execution of the application.

Reversible debugging in a runtime environment

Collecting runtime virtual machine external state for an application running in an application runtime virtual machine, for use in emulation of the application. A method includes identifying application bytecode for which runtime virtual machine external state is to be collected. The method further includes executing machine code generated from the bytecode to generate the runtime virtual machine external state. The method further includes collecting the runtime virtual machine external state. The method further includes storing the runtime virtual machine external state for use in emulating the application.

Collecting application state in a runtime environment for reversible debugging

A debugging and diagnostics system allow for dynamic code generation that inserts code into a production application to identify snappoints that cause snapshots to be taken if predefined conditionals are satisfied. The snappoints are associated with locations in source code for the production application and include conditional statements that must be met to create a snapshot of the production application. The snappoints are used to generate a collection plan that is provided to the server running the production application. The server rewrites the code of the production application based upon the collection plan to insert instructions that create snapshots when the conditional statements are met.

Diagnosing production applications

Dynamically instrumenting code that executes based on a historic execution of a subject executable entity. Historic execution information for a subject executable entity is accessed. The historic execution information includes execution state information for at least one point in time in the historic execution the executable entity. Diagnostic code instruction(s) are identified, for instrumenting subject code instruction(s) of the executable entity. The subject code instruction(s) are virtually executed based at least on supplying the subject code instruction(s) with data from the historic execution information. While virtually executing the identified executable code instruction(s), the diagnostic code instruction(s) are also executed. The diagnostic code instruction(s) collecting diagnostic data regarding the virtual execution of the subject code instruction(s), or override at least one of a value or an execution behavior of the subject code instruction(s).

Dynamic diagnostic code instrumentation over a historic program execution

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

Automatic navigation of a user from a log statement that is selected by the user from a log to a code expression that was executed to generate the selected log statement. The computing system automatically identifies one or more code expressions as candidates for having generated the log statement, and then maps the log statement to these identified candidate code expressions in the code. In response to input from the user selecting the log expression, the computing system uses the mapping to visually provide a correlation between the log statement and the particular code expression, where the one or more mapped code expressions includes the particular code expression that actually generated the log statement.

Automatic navigation from log statement to code

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Bothell, WA, United States of America

Jackson Michael Davis