Method and system for smart test execution in a network test automation platform

1 . A method for smart test execution in a network test automation platform, the method comprising:

retrieving, by a computing device, a set of parameter values for each of a plurality of test cases in a test suite corresponding to a set of risk parameters based on a latest release of a product code;

calculating, by the computing device, a risk index for each of the plurality of test cases based on the set of parameter values and a predefined set of weightage scores;

determining, by the computing device, an execution order of the plurality of test cases based on the calculated risk index;

executing, by the computing device, a set of priority test cases from the plurality of test cases based on the execution order and a predefined threshold risk escape value, wherein:

a risk escape value is a sum of risk indices of unexecuted test cases starting from bottom of the execution order,

upon execution of the set of priority test cases, the risk escape value reaches the predefined threshold risk escape value and execution of remaining of the plurality of test cases is halted, and

the executing the set of priority test cases comprises dynamically modifying the execution order upon a failed execution of a test case to execute one or more related test cases prior to executing subsequent test cases in the execution order; and

dynamically rendering in real-time, by the computing device, one or more live charts based on a current risk escape value upon execution of each of the set of priority test cases via a Graphical User Interface (GUI).

2 . The method of claim 1 , wherein the set of risk parameters comprises defect count, defect age, defect severity, execution count, execution pattern, failed test cases, domain, priority, and effort saving per execution.

3 . The method of claim 1 , further comprising:

scanning, by the computing device, the latest release of the product code and release related documents via an Artificial Intelligence (AI) model; and

identifying, by the computing device, changes in the product code and critical domains from a plurality of domains for test case execution, wherein the critical domains are assigned a higher weightage score than remaining of the plurality of domains during the calculation of the risk index for latest product release.

4 . The method of claim 1 , wherein the one or more related test cases are within a domain of the test case with failed execution.

5 . The method of claim 1 , further comprising predicting, by the computing device, a failed execution of one or more subsequent test cases in the execution order upon the failed execution of the test case using an AI model based on test logs of the test case.

6 . The method of claim 5 , further comprising, for each predicted test case of the one or more predicted subsequent test cases, dynamically modifying, by the computing device, the execution order to execute the one or more related test cases prior to executing the subsequent test cases in the execution order, wherein the one or more related test cases are within a domain of the predicted test case.

7 . The method of claim 1 , further comprising identifying in real-time, by the computing device, errors during execution of the plurality of test cases based on error tags in test logs of the plurality of test cases.

8 . The method of claim 1 , further comprising: monitoring, by the computing device, an error code returned from an Application Programming Interface (API) for each step of the test case; and

identifying, by the computing device, a set of test cases from subsequent test cases to skip execution of the set of test cases based on the error code returned.

9 . A system for smart test execution in a network test automation platform, the system comprising:

a processing circuitry; and

a memory communicatively coupled to the processing circuitry, wherein the memory stores processor instructions, which when executed by the processing circuitry, cause the processing circuitry to:

retrieve a set of parameter values for each of a plurality of test cases in a test suite corresponding to a set of risk parameters based on a latest release of a product code;

calculate a risk index for each of the plurality of test cases based on the set of parameter values and a predefined set of weightage scores;

determine an execution order of the plurality of test cases based on the calculated risk index;

execute a set of priority test cases from the plurality of test cases based on the execution order and a predefined threshold risk escape value, wherein:

a risk escape value is a sum of risk indices of unexecuted test cases starting from bottom of the execution order,

upon execution of the set of priority test cases, the risk escape value reaches the predefined threshold risk escape value and execution of remaining of the plurality of test cases is halted, and

the executing the set of priority test cases comprises dynamically modifying the execution order upon a failed execution of a test case to execute one or more related test cases prior to executing subsequent test cases in the execution order; and

dynamically render in real-time, one or more live charts based on a current risk escape value upon execution of each of the set of priority test cases via a Graphical User Interface (GUI).

10 . The system of claim 9 , wherein the set of risk parameters comprises defect count, defect age, defect severity, execution count, execution pattern, failed test cases, domain, priority, and effort saving per execution.

11 . The system of claim 9 , wherein the processor instructions, when executed by the processing circuitry, further cause the processing circuitry to:

scan the latest release of the product code and release related documents via an Artificial Intelligence (AI) model; and

identify changes in the product code and critical domains from a plurality of domains for test case execution, wherein the critical domains are assigned a higher weightage score than remaining of the plurality of domains during the calculation of the risk index for latest product release.

12 . The system of claim 9 , wherein the one or more related test cases are within a domain of the test case with failed execution.

13 . The system of claim 9 , wherein the processor instructions, when executed by the processing circuitry, further cause the processing circuitry to predict a failed execution of one or more subsequent test cases in the execution order upon the failed execution of the test case using an AI model based on test logs of the test case.

14 . The system of claim 13 , wherein the processor instructions, when executed by the processing circuitry, further cause the processing circuitry to, for each predicted test case of the one or more predicted subsequent test cases, dynamically modify the execution order to execute the one or more related test cases prior to executing the subsequent test cases in the execution order, wherein the one or more related test cases are within a domain of the predicted test case.

15 . The system of claim 9 , wherein the processor instructions, when executed by the processing circuitry, further cause the processing circuitry to identify in real-time, errors during execution of the plurality of test cases based on error tags in test logs of the plurality of test cases.

16 . The system of claim 9 , wherein the processor instructions, when executed by the processing circuitry, further cause the processing circuitry to:

monitor an error code returned from an Application Programming Interface (API) for each step of the test case; and

identify a set of test cases from subsequent test cases to skip execution of the set of test cases based on the error code returned.

17 . A non-transitory computer-readable medium storing computer-executable instructions for smart test execution in a network test automation platform, the computer-executable instructions configured for:

retrieving a set of parameter values for each of a plurality of test cases in a test suite corresponding to a set of risk parameters based on a latest release of a product code;

calculating a risk index for each of the plurality of test cases based on the set of parameter values and a predefined set of weightage scores;

determining an execution order of the plurality of test cases based on the calculated risk index;

executing a set of priority test cases from the plurality of test cases based on the execution order and a predefined threshold risk escape value, wherein:

a risk escape value is a sum of risk indices of unexecuted test cases starting from bottom of the execution order,

upon execution of the set of priority test cases, the risk escape value reaches the predefined threshold risk escape value and execution of remaining of the plurality of test cases is halted, and

the executing the set of priority test cases comprises dynamically modifying the execution order upon a failed execution of a test case to execute one or more related test cases prior to executing subsequent test cases in the execution order; and

dynamically rendering in real-time, one or more live charts based on a current risk escape value upon execution of each of the set of priority test cases via a Graphical User Interface (GUI).

18 . The non-transitory computer-readable medium of claim 17 , wherein the computer-executable instructions are further configured for:

scanning the latest release of the product code and release related documents via an Artificial Intelligence (AI) model; and

identifying changes in the product code and critical domains from a plurality of domains for test case execution, wherein the critical domains are assigned a higher weightage score than remaining of the plurality of domains during the calculation of the risk index for latest product release.

19 . The non-transitory computer-readable medium of claim 17 , wherein the computer-executable instructions are further configured for predicting a failed execution of one or more subsequent test cases in the execution order upon the failed execution of the test case using an AI model based on test logs of the test case.

20 . The non-transitory computer-readable medium of claim 19 , wherein, for each predicted test case of the one or more predicted subsequent test cases, the computer-executable instructions are further configured for dynamically modifying the execution order to execute the one or more related test cases prior to executing the subsequent test cases in the execution order, wherein the one or more related test cases are within a domain of the predicted test case.