Abstract: Some embodiments determine machine configuration intentions from a natural language description of a target machine configuration. Intentions are refined to remove ambiguity, and mapped to pre-approved configuration functions and tasks. A machine configuration task list which invokes the pre-approved configuration functions and tasks is generated by a stabilized language model, and is executed to configure a target machine. The requested target machine is produced without requiring a user or admin to spend substantial effort and time customizing the machine and confirming its security and policy compliance.

Abstract: 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.

Abstract: 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.

Abstract: Historic context data is automatically associated with particular pieces of source code by retrieval data structures. Ephemeral information is preserved, such as how a piece of code originated operationally and was changed over time, which research sources informed the code's origination and changes, and why particular changes in the code were made. Code may be rolled back to an earlier version based on parameters such as whether code had been refactored, or results of testing or static analysis. Rollback goes beyond editor undo actions, and a developer need not specify a timestamp or a version number. Developer documentation burdens are reduced, developer understanding is increased, and code quality is enhanced, by providing ready access to the code's software development context history data. Some actions made possible include highlighting code that was generated automatically by autocompletion or otherwise, highlighting refactored code, and highlighting pasted code, among other actions.

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: 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.

Abstract: 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.

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: 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: 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: 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 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: 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: 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: 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: A collaboration session is provided in which an owner computer system and a participant computer system are both members. Within this session, the collaborators are provided access to a multi-file workspace that is stored locally on the owner computer system. Initially, a set of development tools are identified. These tools are hosted by the owner computer system and are able to operate on the workspace's files. After the tools are identified, they are made accessible to the participant computer system. Later, a request is received from the participant computer system. In some instances, the request is directed to a particular file within the multi-file workspace and is generated using one of the development tools. In this manner, the collaboration session enables the owner computer system's development tools to become accessible to the participant computer system.