Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Flexible yet efficient “find” operations search source code for matches to a general pattern after a developer provides an example string that matches the pattern, without requiring the developer to write a regular expression or script that will implement the desired pattern. Example-driven find-replace functionality uses regular expressions or other pattern match codes, and scripts or other transforms, which are synthesized automatically from examples provided by a developer. This technology allows the developer to focus on workflow inside an integrated development environment instead of breaking focus to search for external documentation, or unfortunately foregoing the flexibility and power of regular expressions and scripts. Synthesizer outputs may be directly or indirectly ranked through user feedback, allowing their refinement. Find match generality may be controlled, e.g., by specifying regex star positions or star counts. Entry of guiding examples may be assisted by autocompletion.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Flexible yet efficient “find” operations search source code for matches to a general pattern after a developer provides an example string that matches the pattern, without requiring the developer to write a regular expression or script that will implement the desired pattern. Example-driven find-replace functionality uses regular expressions or other pattern match codes, and scripts or other transforms, which are synthesized automatically from examples provided by a developer. This technology allows the developer to focus on workflow inside an integrated development environment instead of breaking focus to search for external documentation, or unfortunately foregoing the flexibility and power of regular expressions and scripts. Synthesizer outputs may be directly or indirectly ranked through user feedback, allowing their refinement. Find match generality may be controlled, e.g., by specifying regex star positions or star counts. Entry of guiding examples may be assisted by autocompletion.

Abstract: 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.

Abstract: 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.

Abstract: Assisting automation of repeated edits of code by automated generation of rules that, when applied, perform code transformations. The transformations are synthesized while observing developers make repeated code edits, and automatically perform similar modifications as those observed. This synthesized transformation defines an initial state of code to which the transformation can be applied, and defines a modification from that initial state. A rule is then generated that includes a detector mechanism that, when selected, is configured to find locations in code that have the defined corresponding initial state of the corresponding transformation. Thus, the transformation may be applied to any code to which the rule is exposed.

Abstract: Assisting automation of repeated edits of code by automated generation of rules that, when applied, perform code transformations. The transformations are synthesized while observing developers make repeated code edits, and automatically perform similar modifications as those observed. This synthesized transformation defines an initial state of code to which the transformation can be applied, and defines a modification from that initial state. A rule is then generated that includes a detector mechanism that, when selected, is configured to find locations in code that have the defined corresponding initial state of the corresponding transformation. Thus, the transformation may be applied to any code to which the rule is exposed.

Abstract: 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.

Abstract: The behavior of a machine learning model and the training dataset used to train the model are monitored to determine when the accuracy of the model's predictions indicate that the model should be retrained. The retraining is determined from one or more precision metrics and a coverage metric that are generated during operation of the model. A precision metric measures the ability of the model to make predictions that are accepted by an inference system and the coverage metric measures the ability of the model to make predictions given a set of input features. In addition, changes made to the training dataset are analyzed and used as an indication of when the model should be retrained.

Abstract: Facilitating navigating presentations of data structures in a computing environment. A method includes detecting user input for navigating to a location in a presentation of a data structure stored at a computing system. The method further includes as a result of detecting user input for navigating to a location in the presentation of a data structure, outputting from an accessibility compliant interface at the computing system, at least a portion of a notification identifying the location in the presentation of a data structure. The method further includes, as a result of detecting user input for navigating to a location in the presentation of a data structure, outputting from the accessibility compliant interface at the computing system, at least a portion of an enumeration of tokens for predetermined items of import relevant to the location.

Abstract: 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.

Abstract: 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.

Abstract: Facilitating navigating presentations of data structures in a computing environment. A method includes detecting user input for navigating to a location in a presentation of a data structure stored at a computing system. The method further includes as a result of detecting user input for navigating to a location in the presentation of a data structure, outputting from an accessibility compliant interface at the computing system, at least a portion of a notification identifying the location in the presentation of a data structure. The method further includes, as a result of detecting user input for navigating to a location in the presentation of a data structure, outputting from the accessibility compliant interface at the computing system, at least a portion of an enumeration of tokens for predetermined items of import relevant to the location.