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 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.

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: 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.

Abstract: 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.

Abstract: Generating a template based on source code examples includes identifying a set of related files within a source code project, and identifying a set of textual content portions that are each at least partially repeated across a subset of the related files. A set of templates is generated, each comprising at least one textual content portion from the set of textual content portions. Each template is associated with a set of selection criteria. The set of templates is exposed for automated consumption within a source code editor, based on the set of selection criteria associated with each template. Consuming a template includes identifying a user input indicating creation of a source code block or file within the source code editor, identifying attribute(s) of the source code block or file, and using the attribute(s) to identify a selection criterion associated with a particular template. The particular template is automatically presented.

Abstract: 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.

Abstract: Pasting content from a clipboard buffer as structured tabular data. A computer system determines a data type of content within a clipboard buffer. Based on the data type of the content, the computer system identifies a tabular pattern analysis technique to apply to the content. Based on applying the tabular pattern analysis technique to the content, the computer system identifies a portion of tabular content within the content. Using a clipboard application programming interface, the computer system presents the portion of tabular content to an application as paste data that is structured as a set of rows and a set of columns.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments automate several aspects of document copy-paste updates. An enhanced editor submits context, such as a copied section, pasted section, nearby text, or parser information, to an automatic suggestion generator. The editor gets back a suggestion for automatically changing the pasted section, thus helping users avoid tedium and errors. For instance, string substitutions begun by the user can be automatically and easily completed within the pasted section. Refactoring between variable declarations and parameter lists is detected and completed on request. Situation-specific transforms based on code synthesis, word associations, temporal edit patterns, anchor target lists, regular expressions, or autocompletion are offered. Suggestions are given inside the user's current workflow to avoid breaks in focus. Suggestions can be refined automatically in response to implicit or explicit user feedback. Users are warned of unedited pasted sections. Code review is aided by highlighting pasted sections.

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 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: 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: 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: 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.

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: Systems and techniques for structured query language (SQL) query formatting by examples are described herein. In an example, a SQL query formatter is adapted to receive a SQL statement, such that the SQL statement is formed using SQL tokens. The SQL query formatter may be further adapted to use a SQL formatter model to format the SQL statement, which results in a formatted SQL statement. The SQL formatter model may be trained using at least one previously executed SQL statement. The SQL formatter model may include format definitions for SQL tokens. The SQL query formatter may be further adapted to output the formatted SQL statement.

Abstract: 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.