Abstract: A computer-implemented method includes receiving an original code snapshot corresponding to original code from a first file of a plurality of files. The method also includes receiving a modified code snapshot corresponding to modified code that includes a code modification modifying the original code. The method also includes generating, using a large language model (LLM), refactoring code based on the original code snapshot and the modified code snapshot. The refactoring code is configured to apply the code modification to code from other files of the plurality of files associated with the original code. The method also includes identifying target code from a second file of the plurality of files where the target code is associated with the original code. The method also includes applying the code modification to the identified target code using the refactoring code.

Abstract: A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool includes one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool includes the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool includes the code transformation type of software building tool.

Abstract: A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool comprises one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool comprises the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool comprises the code transformation type of software building tool.

Abstract: A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool comprises one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool comprises the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool comprises the code transformation type of software building tool.

Abstract: A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool comprises one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool comprises the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool comprises the code transformation type of software building tool.

Abstract: A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool comprises one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool comprises the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool comprises the code transformation type of software building tool.

Abstract: A system and method for data extraction from structured documents using historical or related data. Structured documents are searched for instances of an attribute value that match a known historical value for the attribute. Document features associated with the attribute value are identified and anchor a location within the hierarchy of the document structure where the attribute value can be found and extracted. An accuracy for the identified anchors is determined by evaluating how well the anchor's extraction history matches the reported history. Anchors are grouped into anchor sets such that all anchors in a set extract attributes from the same structured document template. The anchors are prioritized according to the determined accuracy, the prioritized list defining the order in which a structure document template should be searched for an attribute value.

Abstract: Provided are methods and systems for monitoring code changes sent for review during the code review process, and performing test coverage calculations on the code changes to generate corresponding test coverage data. A code coverage service monitors code changes input to a code review system and automatically performs test coverage calculations on any code change determined to have significantly evolved as a result of the code review process. The code coverage service provides a notification of the resulting test coverage data (e.g., test coverage number) to various users and/or systems associated with the code change, including the code reviewer, the author of the code, the code review system, other notification systems, and/or any combination thereof. The test coverage data can be utilized in a comprehensive assessment of the code health of the relevant code involved.

Abstract: Provided are methods and systems for monitoring code changes sent for review during the code review process, and performing test coverage calculations on the code changes to generate corresponding test coverage data. A code coverage service monitors code changes input to a code review system and automatically performs test coverage calculations on any code change determined to have significantly evolved as a result of the code review process. The code coverage service provides a notification of the resulting test coverage data (e.g., test coverage number) to various users and/or systems associated with the code change, including the code reviewer, the author of the code, the code review system, other notification systems, and/or any combination thereof. The test coverage data can be utilized in a comprehensive assessment of the code health of the relevant code involved.

Abstract: The mechanical counter-rotation drive according to the present invention is based on the laws of relative motion of an eccentric part of one of at least two shafts, which are constitutive parts of this drive, with respect to the plates of a determined shape whose motion is confined by pins arranged in a determined arrangement, is illustrated in FIG. 1, where in the housing 10 which supports the mechanical counter-rotation drive are accommodated the first (driving) plate 30 and the second (driven) plate 40 in such a manner that they are approximately or completely parallel to one another. In the housing 10 are embedded a plurality of pins 20 of which each one represents a shaft which bears and around which rotates at least one cylinder 35 which is in operational contact with the perimeter 135 of the first plate 30 which is in rigid contact with the first shaft 50 (which shaft is not visible in FIG. 1).

Abstract: The mechanical counter-rotation drive according to the present invention is based on the laws of relative motion of an eccentric part of one of at least two shafts, which are constitutive parts of this drive, with respect to the plates of a determined shape whose motion is confined by pins arranged in a determined arrangement, is illustrated in FIG. 1, where in the housing 10 which supports the mechanical counter-rotation drive are accommodated the first (driving) plate 30 and the second (driven) plate 40 in such a manner that they are approximately or completely parallel to one another. In the housing 10 are embedded a plurality of pins 20 of which each one represents a shaft which bears and around which rotates at least one cylinder 35 which is in operational contact with the perimeter 135 of the first plate 30 which is in rigid contact with the first shaft 50 (which shaft is not visible in FIG. 1).

Abstract: The mechanical counter-rotation drive according to the present invention is based on the laws of relative motion of an eccentric part of one of at least two shafts, which are constitutive parts of this drive, with respect to the plates of a determined shape whose motion is confined by pins arranged in a determined arrangement, is illustrated in FIG. 1, where in the housing 10 which supports the mechanical counter-rotation drive are accommodated the first (driving) plate 30 and the second (driven) plate 40 in such a manner that they are approximately or completely parallel to one another. In the housing 10 are embedded a plurality of pins 20 of which each one represents a shaft which bears and around which rotates at least one cylinder 35 which is in operational contact with the perimeter 135 of the first plate 30 which is in rigid contact with the first shaft 50 (which shaft is not visible in FIG. 1).