Microsoft Technology Licensing, LLC

Arjun Radhakrishna

Mark Alistair Wilson-Thomas

Gustavo Araujo Soares

Sumit Gulwani

Ashish Tiwari

Vu Minh Le

David Ellis Pugh

Peter Groenewegen

Titus Barik

Piyush Arora

Ivan Radicek

Ameya Sanjay Ketkar

Wode Ni

Daniel Morris

Andrew R Sterland

Nachiappan Nagappan

Abhishek Udupa

Ranjita Bhagwan

Gregory Bernard Miskelly

German David Obando Chacon

Shraddha Govind Barke

Jonathan Keith Simmons

Sonu Mehta

Xiang Gao

Aaron Chak Hei Yim

Alan Thomas Leung

Anders Miltner

Mohammad Raza

Kiarash Rahmani

Sahil Garg

A computer implemented method includes obtaining multiple configuration files that include configuration commit histories, detecting patterns in parameter values in the configuration files to generate file-based rules for configuration parameters, detecting patterns in parameter values in the configuration files to generate history-based rules using commit histories for the configuration parameters, and exposing the rules to calling programs.

Pattern base configuration verifier

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

Embodiments use a multi-modal approach to generate software programs that match a solution program description. The solution program description may include natural language, input-output examples, partial source code, desired operators, or other hints. Some embodiments use optimized prompts to a pre-trained language model to obtain initial candidate programs. Maximal program components are extracted and then recombined variously using component-based synthesis. Beam search reduces a solution program search space by discarding some candidates from a given synthesis iteration. Relevance metrics, string similarity metrics, operator frequency distributions, token rareness scores, and other optimizations may be employed. By virtue of optimizations and the multi-modal approach, a solution program may be obtained after fewer iterations than by use of a language model alone. The multi-modal approach is domain agnostic, as illustrated by examples using regular expression and cascading style sheet selector domain specific languages.

Multi-modal program inference

Edit automation using a temporal edit pattern

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

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

In a computer program, sublanguage code snippets implement regular expressions, pattern matching, print formatting, component selection, and other operations, using sublanguage syntax and semantics different from the source code in which snippets are embedded. Writing snippets that give desired execution results has been difficult and interfered with software development workflow. But sublanguage snippet presentation functionality in an enhanced development tool automatically detects source code locations suitable for snippets, receives snippet execution result examples from a developer, submits the examples to synthesis-by-example technology, gets autosynthesized snippets that give those results, and displays snippet insertion candidates with guidance. A snippet selected by the developer replaces the example(s) in the source code, improving developer productivity and program execution accuracy with respect to documented test cases. Examples may be autogenerated, including representative examples or disambiguating examples. Examples may be preserved in comments. Performance criteria for sublanguage snippet presentation are also described.

Autosynthesized sublanguage snippet presentation

Automatically identifying context-specific repeated transformations (such as repeated edit tasks) that are based on observation of the developer drafting or modifying code. As the developer modifies the code, the code passes through a series of states, one after the other. The computing system observes the series of states of the code. It is based on this observation that the computing system identifies repeated transformations of the code for potentially offering to continue performing the repeated transformations for the user. This alleviates the developer from having to manually perform the remainder of the repeated transformations.

Automatic repetition of context-specific code edits

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Seattle, WA, United States of America

Arjun Radhakrishna