Abstract: In an example embodiment, actionable flows are found from customer tickets by autonomously reading and understanding customer queries using neural networks. Specifically, in an example embodiment, a deep neural network is trained to be utilized at two separate stages in a flow generation process. In one stage, the neural network is used to identify a list of repetitive queries from a repository of customer tickets. In another stage, the neural network is used to identify actionable flows from query steps obtained from the list of repetitive queries.

Abstract: Systems and methods are provided for receiving a request for documentation related to functionality of an application, accessing a test automate generated from a recording of actions using the functionality of the application, and parsing the test automate to determine one or more steps in a process and, for each step in the process, a list of the actions for the step and corresponding data. The system and methods further provide for generating a file comprising the one or more steps and the list of actions and corresponding data for each step, determining a selected template for generating documentation for the functionality of the application and generating a populated template by populating one or more steps and corresponding list of actions in the selected template using the file comprising the one or more steps and the list of actions and corresponding data for each step.

Abstract: Systems and methods are provided for receiving a request for documentation related to functionality of an application, accessing a test automate generated from a recording of actions using the functionality of the application, and parsing the test automate to determine one or more steps in a process and, for each step in the process, a list of the actions for the step and corresponding data. The system and methods further provide for generating a file comprising the one or more steps and the list of actions and corresponding data for each step, determining a selected template for generating documentation for the functionality of the application and generating a populated template by populating one or more steps and corresponding list of actions in the selected template using the file comprising the one or more steps and the list of actions and corresponding data for each step.

Abstract: A computer-implemented method can receive a request from a robotic process automation engine to identify a target user interface control element in a webpage represented by a current master data frame. The current master data frame comprises a current document object model (DOM). The method can determine that a target user interface control element identifier associated with the target user interface control element is absent in the current DOM. The method can retrieve an archived version of the target user interface control element from an archived master data frame of the webpage. The method can find an equivalent user interface control element within the current master data frame based at least on the archived version of the target user interface control element, and output an equivalent user interface control element identifier associated with the equivalent user interface control element.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting user interface elements. One example method includes accessing master frame information for a user interface of an application. The master frame information includes first captured user interface information captured during a first execution of a scenario for the application. A subsequent execution of the scenario is performed, including capturing second captured user interface information. A determination is made that the subsequent execution of the scenario has not passed successfully, due to a non-functional error. A determination is made that the non-functional error is based on a failure to locate a user interface element specified in the master frame. A recovery strategy is performed, using the first captured user interface information and the second captured user interface information, to automatically locate the user interface element.

Abstract: A computer-implemented method can receive a request from a robotic process automation engine to identify a target user interface control element in a webpage represented by a current master data frame. The current master data frame comprises a current document object model (DOM). The method can determine that a target user interface control element identifier associated with the target user interface control element is absent in the current DOM. The method can retrieve an archived version of the target user interface control element from an archived master data frame of the webpage. The method can find an equivalent user interface control element within the current master data frame based at least on the archived version of the target user interface control element, and output an equivalent user interface control element identifier associated with the equivalent user interface control element.

Abstract: Systems and methods are provided for determining a first test automate is associated with the same application as a second test automate, and for each action in the first test automate, determining a common action in the second test automate, determining that an element identifier in the first test automate for the common action has changed in the second test automate, and recording the changed element identifier in a data store, the data store comprising a list of changed element identifiers for each of a plurality of applications. The data store is provided so that element identifiers in at least one custom test automate corresponding to the changed element identifiers for at least one application are automatically determined based on the list of changed element identifiers for each of the plurality of applications.

Abstract: In one aspect, there is provided a method for software testing. In one aspect, there is provided a method. The method may include executing a test script including at least one test instruction requiring an input at a user interface element displayed on a screen of a device under test; determining, based on a machine learning model, a candidate location on the screen of the device under test, the candidate location representing a candidate portion of the screen having the user interface element for the required input associated with the at least one test instruction; recognizing, based on optical character recognition, one or more characters in the determined candidate location; selecting, based on the recognized characters, the determined candidate location as the user interface element having the required input; and executing an inserted value at the determined candidate location to test a result of the test script execution.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting user interface elements. One example method includes accessing master frame information for a user interface of an application. The master frame information includes first captured user interface information captured during a first execution of a scenario for the application. A subsequent execution of the scenario is performed, including capturing second captured user interface information. A determination is made that the subsequent execution of the scenario has not passed successfully, due to a non-functional error. A determination is made that the non-functional error is based on a failure to locate a user interface element specified in the master frame. A recovery strategy is performed, using the first captured user interface information and the second captured user interface information, to automatically locate the user interface element.

Abstract: Systems and methods are provided for determining a first test automate is associated with the same application as a second test automate, and for each action in the first test automate, determining a common action in the second test automate, determining that an element identifier in the first test automate for the common action has changed in the second test automate, and recording the changed element identifier in a data store, the data store comprising a list of changed element identifiers for each of a plurality of applications. The data store is provided so that element identifiers in at least one custom test automate corresponding to the changed element identifiers for at least one application are automatically determined based on the list of changed element identifiers for each of the plurality of applications.

Abstract: In one aspect, there is provided a method for software testing. In one aspect, there is provided a method. The method may include executing a test script including at least one test instruction requiring an input at a user interface element displayed on a screen of a device under test; determining, based on a machine learning model, a candidate location on the screen of the device under test, the candidate location representing a candidate portion of the screen having the user interface element for the required input associated with the at least one test instruction; recognizing, based on optical character recognition, one or more characters in the determined candidate location; selecting, based on the recognized characters, the determined candidate location as the user interface element having the required input; and executing an inserted value at the determined candidate location to test a result of the test script execution.

Abstract: A method for polishing a metal layer (20) containing a combination of wide features (12), low density features (14), and high density features (18), is illustrated. A hydrophilic polish pad (24) having a shore D hardness of greater than 50 is used along with slurry (22) containing silica and an acidic based oxidizer such as oxadic acid in a chemical mechanical polishing (CMP) process. The result is less than 5:1 and preferably 1:1. This low selectivity results in the metal layer (20) being polished to a level below the surface of the surrounding oxide in a timed-controlled polish.