Abstract: An area of a graphical user interface that potentially comprises an actionable graphical object is identified. An actional graphical object is an object that generates an event when clicked on. For example, an actional graphical object may be a button, a menu, a menu item, a check box, a text field, a text area, a tab, and/or the like. A cursor movement is generated in the area of the graphical user interface (e.g., using a grid). The cursor movement uses a scanning process to a detect a change in a cursor type (e.g., from an arrow cursor to a link cursor). In response to detecting the change in the cursor type, the actionable graphical object is identified in the area of the graphical interface. The actionable graphical object may then be integrated into a testing process to validate the graphical user interface.

Abstract: Systems and methods include determining a target date; detecting a calendar in a graphical user interface; recognizing a month name in the detected calendar; recognizing one or more date numbers in the detected calendar; identifying a week start day for the detected calendar; identifying a position in the detected calendar associated with the target date based on the identified week start day for the detected calendar and the recognized one or more date numbers in the detected calendar; and automatically selecting the position in the detected calendar associated with the target date.

Abstract: Applications under test (AUT) may be tested by automated testing systems utilizing machine vision to recognize visual elements presented by the AUT and apply inputs to graphical elements, just as a human would. By utilizing the smallest image patch available, processing demands of the testing system are minimized. However, the image patch used to identify a portion of an AUT must be identifiable to the automated system. By selecting image patches that comprise the smallest size, but can be identified in an AUT by an automated system using machine vision, even as the AUT display is resized, reproportioned, noisy, or otherwise altered from the testing platform that was utilized for training.

Abstract: A technique includes receiving, by a computer, user input representing creation of a first programmatic description of a first test object of source code to be tested. The technique includes, in response to receiving the user input, determining, by the computer, based on other programmatic descriptions of other test objects, a recommendation of a parameter to be used in the first programmatic description to identify the first test object. The technique includes causing, by the computer, a display of the recommendation.

Abstract: An image of a graphical user interface is captured. A process is started to record identification of a graphical object (e.g., a text field) in the image of the graphical user interface. Recording identification of the graphical object in the image of the graphical user interface is based on machine learning. An end to the process to record identification of the graphical object in the image of the graphical user interface is identified. A machine learned software object is updated. For example, the machine learned software object is updated with a location of the graphical object. The updated machine learned software object then is used to test the graphical object. For example, to automatically fill in the text field using a test script.

Abstract: Applications under test (AUT) may be tested by automated testing systems utilizing machine vision to recognize visual elements presented by the AUT and apply inputs to graphical elements, just as a human would. By utilizing the smallest image patch available, processing demands of the testing system are minimized. However, the image patch used to identify a portion of an AUT must be identifiable to the automated system. By selecting image patches that comprise the smallest size, but can be identified in an AUT by an automated system using machine vision, even as the AUT display is resized, reproportioned, noisy, or otherwise altered from the testing platform that was utilized for training.

Abstract: A computing device includes a processor and a medium storing instructions. The instructions are executable by the processor to: identify, based on a blob detection analysis, a plurality of potential input elements in a graphical user interface (GUI); determine a set of rows including potential input elements that are in a horizontal alignment and in a same size range; determine a set of columns including potential input elements that are in a vertical alignment and in a same size range; determine a set of input elements comprising multiple potential input elements that are located at intersections of the identified set of rows and the identified set of columns; and perform automated testing of the GUI using the determined set of input elements.

Abstract: A technique includes receiving, by a computer, user input representing creation of a first programmatic description of a first test object of source code to be tested. The technique includes, in response to receiving the user input, determining, by the computer, based on other programmatic descriptions of other test objects, a recommendation of a parameter to be used in the first programmatic description to identify the first test object. The technique includes causing, by the computer, a display of the recommendation.

Abstract: Examples relate to identifying heartbeat messages. In one example, a computing device may: obtain a plurality of messages that includes incoming messages and outgoing messages, each incoming message being sent from a server device to a client device, and each outgoing message being sent from the client device to the server device; identify candidate message pairs, each candidate message pair including one incoming message and one outgoing message; and identify a heartbeat message pair from the candidate message pairs based on at least one of: plurality of timestamps that includes i) incoming message timestamps that each correspond to one of the incoming messages, and ii) outgoing message timestamps that each correspond to one of the outgoing messages; a number of occurrences of each candidate message pair included a message log; or characteristics of data included in the incoming message and outgoing message of each candidate message pair.

Abstract: Identifying transport-level encoded payloads includes identifying transport-level encoded payloads from a recorded script in a display, determining multiple decoding option for at least one of the transport-level encoded payloads where the multiple decoding options includes a recommended chain, and displaying multiple decoding options with at least one of the transport-level encoded payloads.

Abstract: Examples relate to identifying heartbeat messages. In one example, a computing device may: obtain a plurality of messages that includes incoming messages and outgoing messages, each incoming message being sent from a server device to a client device, and each outgoing message being sent from the client device to the server device; identify candidate message pairs, each candidate message pair including one incoming message and one outgoing message; and identify a heartbeat message pair from the candidate message pairs based on at least one of: plurality of timestamps that includes i) incoming message timestamps that each correspond to one of the incoming messages, and ii) outgoing message timestamps that each correspond to one of the outgoing messages; a number of occurrences of each candidate message pair included a message log; or characteristics of data included in the incoming message and outgoing message of each candidate message pair.

Abstract: Identifying transport-level encoded payloads includes identifying transport-level encoded payloads from a recorded script in a display, determining multiple decoding option for at least one of the transport-level encoded payloads where the multiple decoding options includes a recommended chain, and displaying multiple decoding options with at least one of the transport-level encoded payloads.