Automated review of source code for style issues

    公开(公告)号:US11294792B2

    公开(公告)日:2022-04-05

    申请号:US17144443

    申请日:2021-01-08

    Applicant: Rubrik, Inc.

    Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

    AUTOMATED REVIEW OF SOURCE CODE FOR STYLE ISSUES

    公开(公告)号:US20200159643A1

    公开(公告)日:2020-05-21

    申请号:US16196506

    申请日:2018-11-20

    Applicant: Rubrik, Inc.

    Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

    Fileset passthrough using data management and storage node

    公开(公告)号:US11620191B2

    公开(公告)日:2023-04-04

    申请号:US16148991

    申请日:2018-10-01

    Applicant: Rubrik, Inc.

    Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data of a compute infrastructure by generating snapshots of partitions of a fileset of the compute infrastructure and providing a passthrough for storing the snapshots in a data storage separate from the DMS cluster, such as a cloud computing system. In one approach, the DMS nodes determine partitions of a fileset using fileset metadata, generates snapshots of the partitions, and stores the snapshots in the data storage. Each DMS node may include a local storage which is used to facilitate creation of a snapshot of a partition. The snapshot may be removed from the local storage after being sent to the data storage. Rather than storing the snapshot, the DMS cluster stores fileset metadata that is referenced to retrieve the snapshot from the data storage. The snapshot is deployed to retrieve a file stored in the partition.

    Incremental replication of data backup copies

    公开(公告)号:US11422838B2

    公开(公告)日:2022-08-23

    申请号:US16511897

    申请日:2019-07-15

    Applicant: Rubrik, Inc.

    Abstract: An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.

    Automated review of source code for style issues

    公开(公告)号:US10769046B2

    公开(公告)日:2020-09-08

    申请号:US16196506

    申请日:2018-11-20

    Applicant: Rubrik, Inc.

    Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

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