Error assignment for computer programs

    公开(公告)号:US10409667B2

    公开(公告)日:2019-09-10

    申请号:US15624000

    申请日:2017-06-15

    Abstract: An online system identifies an assignment for a computer program error indicated in an error message by applying an assignment model to token sequences identified in the error message. The error message includes a sequence of execution paths of the computer program. Each execution path indicates a function call active in computer memory when the error was generated. In other words, the error message allows tracking of the sequence of nested paths up to the point where the error was generated. In one example, the error message is a stack trace message that reports active stack frames in computer memory during the execution of the program.

    Orchestration of a sequence of computations by external systems

    公开(公告)号:US10853154B2

    公开(公告)日:2020-12-01

    申请号:US16262874

    申请日:2019-01-30

    Abstract: A method is provided for orchestrating a workflow. In some embodiments, the method includes executing a workflow including a first controller that includes a first callout function and a first callback function and corresponds to a first microservice. Execution of the workflow includes execution of the first callout function that causes invocation of the first call to the first microservice. The method further includes receiving a notification of an execution state from the first microservice and transmitting the execution state to the first callback function. The method also includes in response to transmitting the execution state to the first callback function, receiving a function output based on the execution state from the first callback function. The method further includes determining, based on the function output, whether to execute a subsequent controller. The method also includes in response to a determination to execute the subsequent controller, executing the subsequent controller.

    MACHINE LEARNING BASED RANKING OF TEST CASES FOR SOFTWARE DEVELOPMENT

    公开(公告)号:US20190087311A1

    公开(公告)日:2019-03-21

    申请号:US15710127

    申请日:2017-09-20

    Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.

    Machine learning based ranking of test cases for software development

    公开(公告)号:US10474562B2

    公开(公告)日:2019-11-12

    申请号:US15710127

    申请日:2017-09-20

    Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.

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