Granted Patent B2

US 10,642,721 · App. 15/867,293 · Granted May 5, 2020

Inventors: Girish Kulkarni (Bangalore, IN); Sivasankar Ramalingam (Guduvanchery, IN); Chinmaya Ranjan Jena (Bangalore, IN)

Assignee: Accenture Global Solutions Limited

G06F11/3684G06F11/3664G06F11/3688G06N5/02

Abstract

Claims (44)

1. A computer-implemented method executed by one or more processors, the method comprising:

receiving a context file, a test scenario, and a selected automation tool selected through a user interface, the context file including an object map comprising objects that correlate to respective components of a display page for an application, the test scenario describing a test case for the application involving an intended interaction with at least one of the components on the display page;

correlating the intended interaction with the at least one component with the corresponding object in the object map;

processing the intended interaction and the corresponding object through an Artificial Intelligence (AI) model, the AI model trained using training data comprising a plurality of processes and respective process steps supported by the components of the display page;

determining a script template based on the processing and the selected automation tool;

applying, based on the processing, the script template to the intended interaction and the correlated object to generate an automated testing script for the selected automating tool, the automated testing script including placeholder data, wherein the AI model is retrained to replace the placeholder data with the appropriate scripting code, and wherein the placeholder data is replaced with the appropriate scripting code by processing the automated testing script through the AI model; and

executing the automated testing script to test one or more functions of the display page supporting the test scenario.

2. The method of claim 1 , wherein the test scenario includes scenario data used for the intended interaction, and wherein the automated testing script is generated by applying the script template to the scenario data.

3. The method of claim 1 , further comprising:

parsing the test scenario through natural language processing techniques to determine the intended interaction.

4. The method of claim 1 , wherein the test scenario is included in a feature file written in Gherkin.

5. The method of claim 1 , wherein the context file is generated by:

parsing the display page to identify the components, and

logically mapping the components to the respective objects.

6. The method of claim 1 , wherein the automated testing script is generated with default values for the intended interaction.

7. The method of claim 1 , wherein the selected automation tool is one of Unified Functional Testing (UFT), Tricentis Tosca™, Worksoft Certify™, or Selenium™.

8. The method of claim 1 , wherein the AI model is trained through machine learning techniques by applying data regarding the processes to an algorithm.

9. One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving a context file, a test scenario, and a selected automation tool selected through a user interface, the context file including an object map comprising objects that correlate to respective components of a display page for an application, the test scenario describing a test case for the application involving an intended interaction with at least one of the components on the display page;

correlating the intended interaction with the at least one component with the corresponding object in the object map;

processing the intended interaction and the corresponding object through an Artificial Intelligence (AI) model, the AI model trained using training data comprising a plurality of processes and respective process steps supported by the components of the display page;

determining a script template based on the processing and the selected automation tool;

applying, based on the processing, the script template to the intended interaction and the correlated object to generate an automated testing script for the selected automating tool, the automated testing script including placeholder data, wherein the AI model is retrained to replace the placeholder data with the appropriate scripting code, and wherein the placeholder data is replaced with the appropriate scripting code by processing the automated testing script through the AI model; and

executing the automated testing script to test one or more functions of the display page supporting the test scenario.

10. The one or more non-transitory computer-readable storage media of claim 9 , wherein the test scenario includes scenario data used for the intended interaction, and wherein the automated testing script is generated by applying the script template to the scenario data.

11. The one or more non-transitory computer-readable storage media of claim 9 , wherein the operations further comprise:

parsing the test scenario through natural language processing techniques to determine the intended interaction.

12. The one or more non-transitory computer-readable storage media of claim 9 , wherein the test scenario is included in a feature file written in Gherkin.

13. The one or more non-transitory computer-readable storage media of claim 9 , wherein the context file is generated by:

parsing the display page to identify the components, and

logically mapping the components to the respective objects.

14. A system, comprising:

one or more processors; and

a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving a context file, a test scenario, and a selected automation tool selected through a user interface, the context file including an object map comprising objects that correlate to respective components of a display page for an application, the test scenario describing a test case for the application involving an intended interaction with at least one of the components on the display page;

correlating the intended interaction with the at least one component with the corresponding object in the object map;

processing the intended interaction and the corresponding object through an Artificial Intelligence (AI) model, the AI model trained using training data comprising a plurality of processes and respective process steps supported by the components of the display page;

determining a script template based on the processing and the selected automation tool;

applying, based on the processing, the script template to the intended interaction and the correlated object to generate an automated testing script for the selected automating tool, the automated testing script including placeholder data, wherein the AI model is retrained to replace the placeholder data with the appropriate scripting code, and wherein the placeholder data is replaced with the appropriate scripting code by processing the automated testing script through the AI model; and

executing the automated testing script to test one or more functions of the display page supporting the test scenario.

15. The system of claim 14 , wherein the automated testing script is generated with default values for the intended interaction.

16. The system of claim 14 , wherein the selected automation tool is one of Unified Functional Testing (UFT), Tricentis Tosca™, Worksoft Certify™, or Selenium™.

17. The system of claim 14 , wherein the AI model is trained through machine learning techniques by applying data regarding the processes to an algorithm.

18. The system of claim 14 , wherein the test scenario includes scenario data used for the intended interaction, and wherein the automated testing script is generated by applying the script template to the scenario data.

Assignments (1)

Continuity (1)

Cited By (12)

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