Claims:WE CLAIM:
1. A method for optimizing testing of software production incidents, the method comprising:
categorizing, via a processor, an incident ticket received from one or more sources based on one or more pre-defined parameters, the incident ticket corresponding to an obstruction in a software production;
in response to categorization, analyzing, via the processor, the incident ticket using a machine learning algorithm to identify one or more keywords in the incident ticket;
identifying, via the processor, a location of the incident ticket based on the one or more keywords, a test workspace corresponding to the incident ticket based on the location, and a plurality of specific test cases corresponding to the incident ticket based on the test workspace, the identification leading to a first scenario and a second scenario;
in the first scenario, initiating, via the processor, a learning process based on intelligence gathered from a manual processing of the incident ticket; and
in the second scenario,
identifying, via the processor, a test environment for the plurality of specific test cases; and
executing, via the processor, the plurality of specific test cases in the test environment.

2. The method of claim 1, further comprising logging the incident ticket in an incident repository.

3. The method of claim 1, wherein the one or more predefined parameters comprise at least one of a person, a system, and a location related to the incident ticket.

4. The method of claim 1, wherein identifying the location of the incident ticket comprises referring to a keyword-location mapping.

5. The method of claim 1, further comprising routing the incident ticket to the identified location based on at least one of a priority and a severity rating of the one or more keywords and the identified location.

6. The method of claim 1, wherein identifying the test workspace corresponding to the incident ticket comprises referring to a location-workspace mapping.

7. The method of claim 1, wherein identifying the plurality of specific test cases comprises:
identifying a test case location from a plurality of test workspaces by referring to a location-workspace mapping and a search keyword mapping; and
identifying the plurality of specific test cases from the test case location.

8. The method of claim 1, wherein identifying the plurality of specific test cases comprises analyzing a plurality of test cases in the test workspace based on the one or more keywords using the machine learning algorithm.

9. The method of claim 1, further comprising verifying the plurality of specific test cases for suitability to testing of the incident ticket.

10. The method of claim 1, wherein the first scenario corresponds to a negative identification of at least one of the location, the test workspace, and the plurality of specific test cases, and wherein the second scenario corresponds to a positive identification of the location, the test workspace, and the plurality of specific test cases.

11. The method of claim 1, wherein identifying the test environment comprises preparing the test environment corresponding to an environment of the software production and based on the plurality of specific test cases.

12. The method of claim 1, wherein the incident ticket resulting in the first scenario comprises a new incident ticket unrelated to a plurality of past incident tickets and not having at least one of a corresponding location, a corresponding test workspace, and a corresponding specific test case, and wherein the manual processing of the new incident ticket comprises generating at least one of a solution, a location, a test workspace, a test case, and a test environment.

13. The method of claim 1, further comprising updating at least one of a location repository, a test workspace repository, a test case repository, a keyword-location mapping, a location-workspace mapping, and a search keyword mapping based on the learning process.

14. The method of claim 1, further comprising generating a report indicating at least one of a result of the execution, a cause leading to failure of the execution, an effectiveness of implementation of the optimized testing, an area with high number of incident tickets that require implementation of the optimized testing, and a return on investment analysis related to implementation of the optimized testing.

15. A system for optimizing testing of software production incidents, the system comprising:
at least one processor; and
a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
categorizing an incident ticket received from one or more sources based on one or more pre-defined parameters, the incident ticket corresponding to an obstruction in a software production;
in response to categorization, analyzing the incident ticket using a machine learning algorithm to identify one or more keywords in the incident ticket;
identifying a location of the incident ticket based on the one or more keywords, a test workspace corresponding to the incident ticket based on the location, and a plurality of specific test cases corresponding to the incident ticket based on the test workspace, the identification leading to a first scenario and a second scenario;
in the first scenario, initiating a learning process based on intelligence gathered from a manual processing of the incident ticket; and
in the second scenario, identifying a test environment for the plurality of specific test cases, and executing the plurality of specific test cases in the test environment.

16. The system of claim 15, wherein identifying the location of the incident ticket comprises referring to a keyword-location mapping, and wherein identifying the test workspace corresponding to the incident ticket comprises referring to a location-workspace mapping, and wherein identifying the plurality of specific test cases comprises identifying a test case location from a plurality of test workspaces by referring to a location-workspace mapping and a search keyword mapping and identifying the plurality of specific test cases from the test case location.

17. The system of claim 15, wherein the first scenario corresponds to a negative identification of at least one of the location, the test workspace, and the plurality of specific test cases, and wherein the second scenario corresponds to a positive identification of the location, the test workspace, and the plurality of specific test cases.

18. The system of claim 15, wherein the manual processing of the incident ticket comprises generating at least one of a solution, a location, a test workspace, a test case, and a test environment, and wherein the operations further comprise updating at least one of a location repository, a test workspace repository, a test case repository, a keyword-location mapping, a location-workspace mapping, and a search keyword mapping based on the learning process.

19. A non-transitory computer-readable medium storing computer-executable instructions for:
categorizing an incident ticket received from one or more sources based on one or more pre-defined parameters, the incident ticket corresponding to an obstruction in a software production;
in response to categorization, analyzing the incident ticket using a machine learning algorithm to identify one or more keywords in the incident ticket;
identifying a location of the incident ticket based on the one or more keywords, a test workspace corresponding to the incident ticket based on the location, and a plurality of specific test cases corresponding to the incident ticket based on the test workspace, the identification leading to a first scenario and a second scenario;
in the first scenario, initiating a learning process based on intelligence gathered from a manual processing of the incident ticket; and
in the second scenario, identifying a test environment for the plurality of specific test cases, and executing the plurality of specific test cases in the test environment.
Dated these on 3rd day of September, 2015

SWETHA SN OF K& S PARTNERS
AGENT FOR THE APPLICANT
, Description:TECHNICAL FIELD
This disclosure relates generally to software testing, and more particularly to a system and method for optimizing testing of software production incidents.