1. A method for automated testing of an Application Program Interface (API), the method
comprising:
receiving, by an API testing system (103), a test requirement data to test an API from a first database (105);
translating, by the API testing system (103), the test requirement data into a first set of vectors;
selecting, by the API testing system (103), one or more test scripts from a plurality of test scripts stored in a second database (106) based on outputs generated using the first set of vectors provided as inputs to a trained artificial neural network, wherein the outputs are indicative of a probability of effectiveness associated with the one or more test scripts; and
executing, by the API testing system (103), the one or more test scripts to test and validate the API.
2. The method as claimed in claim 1, wherein translating the received test requirement data into the first set of vectors is based on a word to vector model.
3. The method as claimed in claim 1, wherein the artificial neural network is trained based on a supervised learning algorithm using the first database (105) as input and the second database (106) associated with the API testing system (103) as expected output.
4. The method as claimed in claim 1, wherein the one or more test scripts comprises one or more test scenarios, wherein a result of executing the one or more test scenarios from the one or more test scripts is compared with expected result for validation.
5. The method as claimed in claim 1 further comprising generating a plurality of test reports based on the validation of the API, wherein the plurality of test reports comprises at least one of performance results of the tested API, test execution status, and test execution statistics, further the artificial neural network is trained based on plurality of generated test reports.

6. An API testing system (103) for automated testing of an Application Program Interface (API),
the API testing system (103) comprising:
a processor, and
a memory communicatively coupled to the processor, wherein the memory stores the processor-executable instructions, which, on execution, causes the processor to:
receive a test requirement data to test an API from a first database (105); translate the test requirement data into a first set of vectors;
select a one or more test scripts from a plurality of test scripts stored in a second database (106) based on outputs generated using the first set of vectors provided as inputs to a trained artificial neural network, wherein the outputs are indicative of a probability of effectiveness associated with the one or more test scripts; and
execute the one or more test scripts to test and validate the API;
7. The API testing system (103) as claimed in claim 8, wherein the processor is configured to translate the received test requirement data into the first set of vectors is based on a word to vector model.
8. The API testing system (103) as claimed in claim 8, wherein the processor is configured to train the artificial neural network based on a supervised learning algorithm using the first database (105) as input and the second database (106) associated with the API testing system (103) as expected output.
9. The API testing system (103) as claimed in claim 8, wherein the processor is configured to validate the API by comparing a result of executing one or more test scenarios from the one or more test scripts with expected result.
10. The API testing system (103) as claimed in claim 8, wherein the processor is configured to
generate a plurality of test reports based on the validation of the API, wherein the plurality of test
reports comprises at least one of performance results of the tested API, test execution status, and

test execution statistics, further the artificial neural network is trained based on plurality of generated test reports.