Record-replay testing framework with machine learning based assertions

    公开(公告)号:US12038824B2

    公开(公告)日:2024-07-16

    申请号:US17830824

    申请日:2022-06-02

    申请人: ThoughtSpot, Inc.

    IPC分类号: G06F11/36

    CPC分类号: G06F11/3612 G06F11/3688

    摘要: A replay tool configured in a learning mode is used to replay a recorded interaction workflow to obtain respective learning-mode test data responsive to a request from a client device to a server. A baseline response template is obtained from the respective learning-mode test data. A baseline response time of the request is also obtained from the respective learning-mode test data. The recorded interaction workflow is replayed in a testing mode to obtain testing-mode test data. Responsive to determining that a response body included in the testing-mode test data is inconsistent with the baseline response template, a first anomaly message is output. Responsive to determining that the response time included in the testing-mode test data is not within a tolerance of the baseline response time, a second anomaly message is output.

    Auto-triage failures in A/B testing

    公开(公告)号:US12099575B2

    公开(公告)日:2024-09-24

    申请号:US17942234

    申请日:2022-09-12

    申请人: ThoughtSpot, Inc.

    摘要: First images that are screenshots from a first version of a software component are obtained. Second images that are screenshots from a second version are obtained. A collection of image deviations that includes pair-wise image deviations between pairs of images are identified. A pair of images includes a first image from the first images and a corresponding second image from the second images. An image deviation indicates a portion of the second image identified as differing from a spatially corresponding portion of the first image. The image deviations are grouped into deviation groups. At least some of the second images are associated with at least some of the deviation groups. A subset of the second images corresponding to a deviation group is output responsive to a selection of an indication of the deviation group.

    RECORD-REPLAY TESTING FRAMEWORK WITH MACHINE LEARNING BASED ASSERTIONS

    公开(公告)号:US20230393963A1

    公开(公告)日:2023-12-07

    申请号:US17830824

    申请日:2022-06-02

    申请人: ThoughtSpot, Inc.

    IPC分类号: G06F11/36

    CPC分类号: G06F11/3612 G06F11/3688

    摘要: A replay tool configured in a learning mode is used to replay a recorded interaction workflow to obtain respective learning-mode test data responsive to a request from a client device to a server. A baseline response template is obtained from the respective learning-mode test data. A baseline response time of the request is also obtained from the respective learning-mode test data. The recorded interaction workflow is replayed in a testing mode to obtain testing-mode test data. Responsive to determining that a response body included in the testing-mode test data is inconsistent with the baseline response template, a first anomaly message is output. Responsive to determining that the response time included in the testing-mode test data is not within a tolerance of the baseline response time, a second anomaly message is output.

    Auto-Triage Failures In A/B Testing
    4.
    发明公开

    公开(公告)号:US20240086495A1

    公开(公告)日:2024-03-14

    申请号:US17942234

    申请日:2022-09-12

    申请人: ThoughtSpot, Inc.

    IPC分类号: G06K9/62 G06F3/0484 G06F8/71

    摘要: First images that are screenshots from a first version of a software component are obtained. Second images that are screenshots from a second version are obtained. A collection of image deviations that includes pair-wise image deviations between pairs of images are identified. A pair of images includes a first image from the first images and a corresponding second image from the second images. An image deviation indicates a portion of the second image identified as differing from a spatially corresponding portion of the first image. The image deviations are grouped into deviation groups. At least some of the second images are associated with at least some of the deviation groups. A subset of the second images corresponding to a deviation group is output responsive to a selection of an indication of the deviation group.