Microsoft Technology Licensing, LLC

Mark Alistair Wilson-Thomas

David Ellis Pugh

Gustavo Araujo Soares

Arjun Radhakrishna

German David Obando Chacon

Sumit Gulwani

Vu Minh Le

Peter Groenewegen

Kesavan Shanmugam

Ashish Tiwari

Shengyu Fu

Simon Calvert

Jonathan Daniel Keech

Vivian Julia Lim

Piyush Arora

Titus Barik

Nachiappan Nagappan

John Sells Tilford

Srivatsn Narayanan

Deborah Chen

Ivan Radicek

Zhongyan Guan

Jonathan Keith Simmons

Ameya Sanjay Ketkar

Wode Ni

Neelakantan Sundaresan

Alexey Svyatkovskiy

Andrew R Sterland

Abhishek Udupa

Shao Kun Deng

Chetan Bansal

Christian Alma Bird

Gregory Bernard Miskelly

Marc Manuel Johannes Brockschmidt

Miltiadis Allamanis

Thomas Michael Josef Zimmermann

Shuo Zhang

Jonathan Preston Carter

Guillermo Serrato Castilla

Hemchander Venkateshwara Sannidhanam

Nikitha Rao

Shraddha Govind Barke

Xiang Gao

Anqi Li

Alan Thomas Leung

Christian David Gunderman

Jui Hanamshet

Joshua Bates Stevens

Shwetha Srinath

Jin Woo Jang

Anders Miltner

Aaron Chak Hei Yim

Some embodiments automatically detect a software development code improvement stage. Improvement stage detection may be based on computational events involving a development tool, such as a testing tool, a debugger, or a performance profiler. Program analysis tools driven by artificial intelligence functionality may then be automatically invoked to provide code improvement options, which may be presented to a developer in a tool user interface. Options may include source code edits, configuration changes, or test coverage changes, for example. Analysis results and corresponding code improvement options are thus presented when the developer is prioritizing program performance, program behavior accuracy, program security, or programming style, as opposed to prioritizing code creation or code integration. Programs under development, as well as quality reviews of such programs, may accordingly be optimized by performing performance and security analysis, testing, and coding style analysis during the code improvement stage.

Software development improvement stage optimization

During software development, embodiments find various kinds of weak spots in source code and automatically suggest fixes to strengthen the code, without requiring developers to expressly select weakness finder mechanisms or fixer mechanisms by navigating a development tool's menu system. Weakness finders may analyze code using items such as hole detection, diagnostic errors, test results, changed code matches, prospective code discrepancies, generated code confidence scores, generated suggestion competition, and artificial intelligence. Weak spots and their context are submitted to weak spot fixers, which may generate fix suggestions using functionalities such as code synthesis, refactoring, autocompletion, retesting, and artificial intelligence. Fix candidate sets may be evaluated for consistency, diagnostic errors, and discrepancies. Snippets may be dynamically filled for presentation to a user.

Syntax subtree code strengthening

Edit automation functionality generalizes edits performed by a user in a document, locates similar text, and recommends or applies transforms while staying within a current workflow. Source code edits such as refactoring are automated. The functionality uses or provides anchor target lists, temporal edit patterns, edit graphs, automatable edit sequence libraries, and other data structures and computational techniques for identifying locations appropriate for particular edits, for getting transforms, for selecting optimal transforms, for leveraging transforms in an editing session or later, and for displaying transform recommendations and results. The edit automation functionality enhances automation subtool generation, discoverability, and flexibility, for refactoring, snippet insertion, quick actions in an integrated development environment, and other automatable edit sequences.

Edit automation using an anchor target list

Some embodiments determine automatically which synthesized or otherwise autocreated suggestions for source code editing are presented to developers. Some filter out autocreated coding suggestions that have not been sufficiently endorsed by a developer's team, based on a suggestion trust score. The trust score may reflect the suggestion's adoption in a particular repository or codebase, or affiliation of the suggestion with a library release, or an actual or implied review of the suggestion by team members. Some suggestion filters enhance existing development team code review practices, by offering endorsed suggestions in autocompletion or analysis interfaces of tools and by withholding non-endorsed suggestions from display. Context illustrating the autocreated suggestion's provenance may be displayed to help developers decide whether to adopt the suggestion themselves while editing code. Some tools that are enhanced with suggestion filtering functionality avoid developer configuration burdens while increasing consistent adoption of endorsed suggestions inside a codebase.

Software development autocreated suggestion provenance

Edit automation using a temporal edit pattern

Distinguishing pattern differences from non-pattern differences. A set of differences is identified. The set comprises a plurality of differences between first and second versions of a document. A pattern is identified. The pattern explains a transformation from a first string in the first version of the document to a second string in the second version of the document. A subset of differences are identified. The subset comprises a plurality of differences, from among the set, which match the pattern. While presenting a user interface that visually highlights differences between the first and second versions of the document, a first visual treatment is applied to a first difference, based on the first difference being included in the subset. A second visual treatment is also applied to a second difference, based on the second difference being excluded from the subset. The second visual treatment is different than the first visual treatment.

Distinguishing pattern differences from non-pattern differences

Some embodiments automatically assess source code merge conflict resolution candidates and then display a recommendation explaining strengths and weaknesses of each candidate. Thus informed, a developer can avoid adopting changes which produce merged code that does not build or does not pass tests, and avoid merged code that departs from a pattern followed in previous changes to a code base. Candidate conflict resolution assessments depend on objective criteria such as merged code buildability, operational accuracy, edit pattern consistency, or conflict resolution generator confidence. Candidates may be automatically ranked, prioritized, or rejected outright by an embodiment. Work item descriptions and candidate provenance may be displayed to aid developer selection of an optimal merge candidate.

Source code merge conflict resolution

Methods, systems, and computer program products for deploying a static code analyzer based on program synthesis from input-output examples. A computer system uses program synthesis on a set of input-output examples of source code edits to generate a rewrite rule that defines a transformation pattern. Based on a determined static code analyzer format, the computer system generates a static code analyzer from the rewrite rule. The static code analyzer includes a find portion that indicates a matching condition for identifying a portion of source code text, and a fix portion that indicates a textual replacement to apply to the portion of source code text matched by the find portion. The computer system deploys the static code analyzer to a development environment, including configuring the static code analyzer to be executable against a source code file within the development environment.

Deploying a static code analyzer based on program synthesis from input-output examples

Edit automation enhancements may be implemented in source code editors and other text editors. Provisional selections that indicate user intentions are submitted to a suggestion generator with other edit context information, to improve the quality of generated text suggestions and reduce the cognitive load on users. A provisional selection may include a highlighted completion list entry, or document text targeted by a hovering cursor, or metainformation text targeted by the hovering cursor, for example. An inline grey text suggestion driven by provisional selection may be displayed simultaneously with completion list suggestions that were created without regard to provisional selection. Suggestions driven by provisional selection may be interleaved with existing document text. Suggestions may be accepted fully in one gesture, or in parts. Suggestions may be edited by a user before being accepted, driving further suggestion refinement. Multiple suggestions may be displayed simultaneously, reducing pressure on the suggestion generator.

Provisional selection drives edit suggestion generation

Access control enhancements reduce security risks and management burdens when a user with access to a primary asset seeks access to a related supplementary asset. When a sufficient proof of access to the primary asset is provided, and the relationship of the primary and supplementary assets is recognized, access to the supplementary asset is granted without requiring a separate sign-in, a permission query to the supplementary asset's owner, or an authorization through an authenticated identity of the requestor, for example. Automatic access to the supplementary asset can be granted without the security risks inherent in a file share or a share link. In particular, a developer with access to one component of a project can be automatically and conveniently granted access to the rest of the project. Likewise, a custom machine learning model for autocompletion becomes accessible to all developers working on the repository source used to train the model.

Related asset access based on proven primary asset access

A natural language code search service provides idioms or frequently-occurring code patterns for a code fragment based on similar type usage and method/API invocation usage. The search service uses a data mining technique that mines code snippets found from various websites and code snippets generated from a neural model to detect idioms in the code snippets that were previously unknown and which can be reused. A search is initiated through a natural language query within a code development tool or application thereby avoiding the need to switch out of the current application to perform the search.

Natural language code search

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

A synthesis procedure learns program transformations for a text document, on-the-fly during an edit session, from examples of concrete edits made during the edit session and from an unsupervised set of additional inputs. The additional inputs are derived from explicit feedback from the user and inferred feedback from the user's behavior during the edit session. A reward score, based on anti-unification and provenance analysis, is used to classify the additional inputs as either a positive input or a negative input. Outputs are generated for the positive inputs that are consistent with the existing examples and then used to synthesize a new program transformation. The program transformations are then used to generate code edit suggestions during the edit session.

Feedback-driven semi-supervised synthesis of program transformations

Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Providing custom machine-learning models

Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics. Search intent detection permits efficient and effective use of a single search box for a wide variety of different searches for different kinds of results, thereby simplifying a development tool user interface.

Extraquery context-aided search intent detection

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Mercer Island, WA, United States of America

Mark Alistair Wilson-Thomas