Abstract: Techniques for intelligent application source code testing prioritization are disclosed. In one embodiment, a method is disclosed comprising obtaining execution history information associated with a number of test cases and source code change history information, generating a plurality of encodings for a number of test cases, each test case encoding comprising a merger of a test case description and source code change history information corresponding to a test case, determining a failure propensity score for each test case using a trained failure prediction model and the test case's encoding, determining a prioritization of the test cases in accordance with the failure propensity scores, such that a test case having a greater failure propensity score indicating a greater propensity to fail having a higher priority than another test case with a lower propensity to fail, and generating a testing schedule for the plurality of test cases in accordance with the test case prioritization.

Abstract: In an example, a plurality of images of an interface of an application may be identified. An image of the plurality of images may be compared with a second image of the plurality of images to determine a first set of regions, of the interface, in which the second image is different than the first image. Based upon the first set of regions, a set of dynamic regions of the interface may be determined. A fourth image of the interface may be identified. Based upon the set of dynamic regions, a first portion of the fourth image may be compared with a first portion of the first image to determine user interface testing information associated with the interface of the application.

Abstract: In an example, images of an interface of an application may be identified. The images may be clustered into groups associated with layouts of the interface. An image of a first group associated with a first layout may be compared with a second image of the first group to determine a first set of regions, of the interface, in which the second image is different than the first image. Based upon the first set of regions, a set of dynamic regions of the interface may be determined. A fourth image of the interface may be identified. The fourth image may be determined to correspond to the first layout of the interface. Based upon the set of dynamic regions, a first portion of the fourth image may be compared with a first portion of the first image to determine user interface testing information associated with the interface of the application.

Abstract: In an example, a plurality of images of an interface of an application may be identified. An image of the plurality of images may be compared with a second image of the plurality of images to determine a first set of regions, of the interface, in which the second image is different than the first image. Based upon the first set of regions, a set of dynamic regions of the interface may be determined. A fourth image of the interface may be identified. Based upon the set of dynamic regions, a first portion of the fourth image may be compared with a first portion of the first image to determine user interface testing information associated with the interface of the application.

Abstract: Techniques for intelligent application source code testing prioritization are disclosed. In one embodiment, a method is disclosed comprising obtaining execution history information associated with a number of test cases and source code change history information, generating a plurality of encodings for a number of test cases, each test case encoding comprising a merger of a test case description and source code change history information corresponding to a test case, determining a failure propensity score for each test case using a trained failure prediction model and the test case's encoding, determining a prioritization of the test cases in accordance with the failure propensity scores, such that a test case having a greater failure propensity score indicating a greater propensity to fail having a higher priority than another test case with a lower propensity to fail, and generating a testing schedule for the plurality of test cases in accordance with the test case prioritization.