Micro Focus Lcc

Entit Software LLC

Peng-Ji Yin

Jie Zhang

Er-Xin Shang

Shuhui Fu

Gaoyang Zhou

Cheng Hua

Moshe Eran Kraus

Hua-Ming Zhai

Yang Luo

Amos Nesher

Oded Keret

Leonid Pekel

Gil Nakache

Anton Kaminsky

Yun-Sheng Liu

YunSheng Liu

ChengZhe Xu

Eyal Luzon

Tsachi Ben Zur

Avishai Moshka

Yi-Bin Guo

Xiao-Fei Yu

An apparatus may include a processor () that may generate automated test scripts to test graphical user interface (GUI) functions of an application under test (AUT) (). The apparatus may generate a screen element () that is overlaid onto at least one or all of the GUIs (). The screen element () may therefore receive user inputs directed at the GUI (), but block such inputs from being provided to the GUI (). The user inputs received at the screen element () may be recorded in an automated test script for later replay. Blocking the user input may prevent a change in appearance of a GUI element () that would otherwise result from the user input, facilitating automated location of the GUI element () during replay of the automated test script.

Recording test usage of a graphical user interface with blocked inputs

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.

Using mouseover to scan a graphical user interface to improve accuracy of graphical object recognition

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.

Machine learning based on functional testing

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.

Automated identification of input elements in a graphical user interface

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.

Systems and methods of automatically identifying a date in a graphical user interface

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.

Automated application testing of mutable interfaces

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.

Recommending programmatic descriptions for test objects

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.

Identifying heartbeat messages

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.

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Peng-Ji Yin