Microsoft Technology Licensing, LLC

John Sells Tilford

Daniel R Dole

David Ellis Pugh

Monty Hammontree

Brandon C Adams

Steven Clarke

Zachary S Zaiss

Murali Krishna Hosabettu Kamalesha

Oleg Tkachenko

Randy S Kimmerly

Mark Alistair Wilson-Thomas

Simon Calvert

Vijaye Ganesh Raji

Kesavan Shanmugam

Deborah Chen

Miltiadis Allamanis

Marc Manuel Johannes Brockschmidt

Srivatsn Narayanan

Guillermo Serrato Castilla

Joshua Bates Stevens

Improved techniques for asynchronously displaying the results of a codebase analysis service are provided herein. Initially, machine learning is applied to a corpus of model code. In doing so, a machine learning model is generated, where the model identifies coding practices that are included in the corpus of model code. After this model is generated, then the model is applied to a current codebase by comparing coding practices of the current codebase to the identified coding practices that were extracted, or rather identified, from the corpus of model code. Then, in response to detecting one or more differences between the current codebase's coding practices and the identified coding practices, where the differences satisfy a pre-determined difference threshold, a user interface is caused to display one or more insights. These insights beneficially provide additional detailed information describing the differences.

Displaying results of a codebase analysis service

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

Disclosed herein is a system and method for optimizing a developer's ability to find and navigate relevant documents, relationships, and other information related to an identifier in the code they are developing. An inline viewport is presented to the user in response to the user selecting an identifier in the code whereby the user is able to see relevant information related to the identifier in a spatially consistent location with the code they are investigating. Further, the developer has the ability to make changes to the code in the viewport without leaving the current editor.

Inline display and preview of related information for elements in a document

Visual elements can be arbitrarily rendered in a code editor. Visual elements can be rendered at any location on a display. Visual elements can include text, images, graphics, video, and/or multi-media. Rendering of visual elements will not obstruct a view of rendered text. Nor will rendering of the visual element obstruct a view of the visual element. Visual elements can be rendered between lines of text and/or in-line with text. If a selected location will obstruct a view or either rendered text or another visual element, the rendered text and/or the other visual element is reconfigured to allow space for rendering the visual element.

Arbitrary rendering of visual elements on a code editor

Embodiments consistent with the subject matter of this disclosure may transparently project text fragments from one or more buffers into one or more projection buffers by reference, instead of by copying. Clients or applications, such as, for example, compilers, language services, an editor's rendering system, or other client or application, may transparently process the projected text fragments projected into the projection buffers as if processing simple text files. In some embodiments, text of a first language, embedded in a document having text of one or more other languages, may be projected to a projection buffer and provided to an application, which may process text of the first language. In other embodiments, text may be elided from a document by projecting to a projection buffer only text not to be elided from the document.

Multisource composable projection of text

Multiple independent classifiers of a code editor are used to classify a range of text. Each classifier is an independent classifier and can independently classify any given range of text. Classifiers can be added and merged during run time of the code editor. Adding a classifier does not require modification of any existing classifiers in the code editor. Text can be classified and formatted in accordance with any number of classifiers in the aggregate. The user of the code editor can initiate classification. The user of the code editor can select text for user specific formatting.

Classifying text in a code editor using multiple classifiers

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Seattle, WA, United States of America

John Sells Tilford