Microsoft Technology Licensing, LLC

Srivatsn Narayanan

Kesavan Shanmugam

Mark Alistair Wilson-Thomas

Simon Calvert

Erich Gamma

Jason Ronald William Ramsay

Michael C Fanning

Shengyu Fu

David Ellis Pugh

Miltiadis Allamanis

Marc Manuel Johannes Brockschmidt

John Sells Tilford

Laurence Jack Golding

Deborah Chen

Jonathan Daniel Keech

Jonathan Preston Carter

Vivian Julia Lim

Guillermo Serrato Castilla

Alexandru Ioan Dima

Gen Lu

Charles Eric Lantz

Guillaume Jenkins

Dirk Baumer

Joshua Bates Stevens

Jinu Joseph

David Andrew Knise

Anuradha Sharma

Daniel Lebu

Anthony Van Der Hoorn

A computer system is configured to identify a software development environment (SDE), and generate a first prebuild of the SDE. The computer system is further configured to obtain data associated with a plurality of elements that are related to a state of the SDE, and generate a first hash based on data associated with the plurality of elements. The computer system is also configured to identify a commit to the SDE and determine that the commit changed the state of SDE by obtaining data associated with the plurality of elements, generating a second hash based on the data, and determining that the second hash is different from the first hash. In response to determining that the first commit changed the state of the SDE, the computer system then generates a second prebuild of the SDE.

Software development environment prebuild triggered by environment change commits

Tracking edits executed against a file to ensure that the edits are monitored consistently so that language service requests are properly handled. Initially, a collaboration session is established. This collaboration session includes an owner and a participant computer system. Then, the owner computing system receives messages that are directed toward a file stored by the owner computer system. These messages include edits that are to be performed against the file and language service request(s). A file version is then assigned to a subset of these edits. As the subset of edits are executed against the file, the file's state changes. The file versions are published to both the participant computer system and to a language service running on the owner computer system. The language service uses the published file versions to track the edits that are being executed against the file and to respond to the language service request(s).

Responding to requests by tracking file edits

A collaboration session is provided in which an owner computer system and a participant computer system are both members. While working within this session, the participant computer system is provided access to a multi-file workspace that is stored locally on the owner computer system. The owner computer system receives a request from the participant computer system. The request is used to gain access to the owner computer system's language service. In response to this request, the owner computer system remotes its language service so that the language service is accessible to the participant computer system.

Architecture for remoting language services

Improving the results and process of machine learning service in computer program development. A client's codebase is accessed. A set of features are extracted from the client's codebase. One or more features from the set of features are then selected. Thereafter, at least one of the selected features is sent to a machine learning service that uses the received feature(s) to build custom model(s) for the client's computer system.

Extracting program features for assisting software development

Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.

Code development using continued machine learnings

A data analysis and transformation engine provides a service that automatically analyzes, formats, and/or reviews changes made to collection of artifacts stored in one or more source control systems in accordance with a user's instructions in a coordinated manner. A user subscribes to the data analysis and transformation engine with instructions on the user's preference for formatting, reviewing, and analyzing an artifact after the artifact was modified and checked into a source control system.

Transactional distributed data analysis and transformation

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Srivatsn Narayanan