Microsoft Technology Licensing, LLC

Andrew R Sterland

Timothy Scott Rice

Jackson Michael Davis

Jordi Mola

Michael C Fanning

Adam D Nathan

Thomas Lai

Del Myers

Leslie Yvette Richardson

James M Pinkerton

Patrick Nelson

John I Montgomery

Michael J Leonard

Gregg Bernard Miskelly

Robert Alan Paveza

Christopher Douglas Edmonds

Felix Gerard Torquil Ifor Andrew

Juan Carlos Arevalo Baeza

Simon Calvert

Kenneth W Sykes

Elliot H Omiya

Gregory Bernard Miskelly

Deborah Chen

Harold L Pierson, Iii

Daniel James Moseley

Benjamin James Anderson

Francisco Javier Wong

Zhaochuan Gao

Andriy Stasyuk

Patrick Chi Wai Wong

Jeffrey T Fisher

Taylor C Woll

Karthik Vivek Nadig

Tae Hyung Kim

Steven E Lucco

Randy S Kimmerly

Mark Alistair Wilson-Thomas

David Ellis Pugh

Gustavo Araujo Soares

Mark Marron

Arjun Radhakrishna

German David Obando Chacon

Peter Groenewegen

Nikhil Khandelwal

Akrosh Gandhi

Nikolaus Lee Karpinsky

Christopher M H Faucon

Piyush Arora

Mark Anthony Jelf Downie

Andrew Joseph Luhrs

Timothy Gardner Misiak

Patrick Fenelon

Esteban Herrera

Aaron Chak Hei Yim

Wai Hang (“Barry”) Tang

Patrick Lothian Nelson

Harold L Iii Pierson

Gregg Miskelly

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

Embodiments automate surfacing of underutilized development tool features, thereby enhancing the discoverability of subtools, commands, shortcuts, settings, visualizers, and other tool features. After spotting an inefficiency in the user's interaction with one or more tools, the feature surfacing functionality offers the user an interaction optimization suggestion. A mapping structure correlates detected interaction patterns with objectively better interaction optimizations. Several examples of mappings are discussed. The user can accept a suggestion, have the suggested optimization applied by an enhanced tool, and thereby reduce the number of user gestures utilized to accomplish a desired result, reduce the number of tools utilized, increase security, reduce risk of error, or get to the desired result faster, for example. Interaction optimizations also help the user stay focused, by reducing or avoiding departures from the user's current primary workflow. Other aspects of tool feature surfacing functionality are also described herein.

Surfacing underutilized tool features

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

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

The present invention extends to methods, systems, and computer program products for stepping through JavaScript code in a debugger without landing on errors in library or open source code. A debugger receives user inputs designating one or more segments of the JavaScript code as library code. The debugger then performs debugging operations on the JavaScript code. The debugging operations including a stepping operation for stepping through the JavaScript code to identify errors only in user-generated segments of the JavaScript code, wherein the user-generated segments correspond to code that was not designated as library code.

Javascript debugging using just my code

A method for diagnosing computer readable instructions related to transfers of control is disclosed. A state transition of a unit of execution within a logical operation providing an event handler for a Document Object Model is logged during runtime of the computer readable instructions. Diagnostic information of the logical operation is associated with the state transition. The state transition and associated diagnostic information is provided for retrieval at a selected point in the computer readable instructions.

Diagnostics of state transitions

A method for diagnosing computer readable instructions related to transfers of control is disclosed. A state transition of a unit of execution within a logical operation is logged during runtime of the computer readable instructions. Diagnostic information of the logical operation is associated with the state transition. The state transition and associated diagnostic information is provided for retrieval at a selected point in the computer readable instructions.

Techniques are described herein that are capable of instantiating and executing in-process wrapped execution engine(s) for tooling a dynamic program across a process boundary. For instance, a dynamic language execution engine may be instantiated in a wrapper object that is hosted in a host process to provide a wrapped execution engine while the host process is running. The wrapped execution engine may be configured to provide information regarding execution of a targeted application of the host process to a requesting application. The wrapped execution engine may be isolated from other execution engine(s) that are included in the host process such that the targeted application does not have access to code that defines the wrapped execution engine. The targeted application may include one or more of the other execution engines.

In-process wrapped execution engine(s) for tooling a dynamic program across a process boundary

Techniques are described herein that are capable of constructing an inter-process communication channel, for example, between a requesting component and a targeted process. The communication channel may have one or more links, such that each link is defined by a respective node pair in a plurality of nodes. The plurality of nodes includes the requesting component, the targeted process, and optionally one or more intermediate nodes. An attachment request is received via a user interface. The requesting component is attached to the targeted process. For instance, the links may be traversed to discover the targeted process. The communication channel is established. For instance, in each node of a subset of the plurality of nodes, node information may be retained to indicate a next consecutive node of the plurality of nodes to which messages from the requesting component to the targeted process are to be delivered.

Inter-process communication channel

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Seattle, WA, United States of America

Andrew R Sterland