Method and system for providing test and regression automation

1 . A method for providing test and regression automation via a cloud native elastic tool, the method being implemented by at least one processor, the method comprising:

obtaining from a module comprising an artificial intelligence (AI) module at least one testing condition from at least one test configuration corresponding to at least one test suite;

implementing at least one machine learning (ML) model via the cloud native elastic tool operating in a computing environment that comprises a distributed computing environment;

training the at least one ML model based on a cross-validation technique and a holdout technique in operating within a predetermined range of error ratings that corresponds to at least one software testing parameter based on the at least one testing condition associated with at least one testing error involving instabilities of the computing environment comprising a cluster instability and an infrastructure instability;

executing the at least one test suite based on the at least one software testing parameter via the cloud native elastic tool;

performing within the cloud native elastic tool an automated software verification process comprising a regression algorithm on different branches of source code software of a source code repository hosting service to obtain at least one result based on the executing the at least one test suite; and

outputting electronically via a graphical user interface a report for the at least one test suite via the cloud native elastic tool, the report including information that corresponds to the at least one software testing parameter, an execution status, and the at least one result.

2 . The method of claim 1 , further comprising:

detecting the at least one testing error that corresponds to the executing of the at least one test suite, the at least one testing error including a testing failure due to the instabilities of the computing environment; and

automatically initiating via the cloud native elastic tool an action to re-execute the at least one test suite based on an analysis of the at least one testing error.

3 . The method of claim 2 , further comprising:

providing at least one alert when the at least one testing error is detected, the at least one alert including information that relates to the at least one testing error, the automatically initiated action, and a determined origin of the at least one testing error; and

notifying at least one responsible user with the at least one alert.

4 . The method of claim 1 , wherein the at least one test configuration defines at least one dependency and at least one testing sequence priority for each of a plurality of test cases that corresponds to the at least one test suite, each of the plurality of test cases including at least one predefined input and at least one predefined, expected output.

5 . The method of claim 1 , wherein the at least one software testing parameter facilitates execution of the at least one test suite on at least one from among a cloud computing environment and an on-premise computing environment, and

wherein the an adjustment of at least one dependency and at least one testing sequence priority for each of a plurality of test cases that corresponds to the at least one test suite.

6 . The method of claim 1 , further comprises:

determining, by the at least one ML model, at least one predicted dependency for each of a plurality of test cases that corresponds to the at least one test suite;

determining, by the at least one ML model, at least one predicted outcome for each of the plurality of test cases; and

determining, by the at least one ML model, at least one potential point of failure and a corresponding cause for each of the plurality of test cases.

7 . The method of claim 1 , wherein the error ratings comprise at least one among from a least square error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate.

8 . The method of claim 1 , wherein the at least one test suite is executed based on a predetermined schedule that is defined in the at least one test configuration, the predetermined schedule including at least one from among a continuous testing schedule and an intermittent testing schedule.

9 . The method of claim 1 , wherein the automated software verification to obtain the at least one result further comprises:

automatically comparing each of the at least one result with a corresponding output of a secondary execution of the at least one test suite, the secondary execution relating to secondary testing of the at least one test suite from a different branch of a source code repository hosting service;

automatically determining based on the regression and a validation analysis automation via the cloud native elastic tool, a verified status for each of the at least one result based on an outcome of the comparing, the verified status including at least one from among a passed status and a failed status; and

automatically marking each of the at least one result with the corresponding verified status.

10 . A computing device configured to implement an execution of a method for providing test and regression automation via a cloud native elastic tool, the computing device comprising:

a processor;

a memory; and

a communication interface coupled to each of the processor and the memory,

wherein the processor is configured to:

obtain from a module comprising an artificial intelligence (AI) module at least one testing condition from at least one test configuration corresponding to at least one test suite;

implement at least one machine learning (ML) model via the cloud native elastic tool operating in a computing environment that comprises a distributed computing environment;

train the at least one ML model based on a cross-validation technique and a holdout technique in operating within a predetermined range of error ratings that corresponds to at least one software testing parameter based on the at least one testing condition associated with at least one testing error involving instabilities of the computing environment comprising a cluster instability and an infrastructure instability;

execute the at least one test suite based on the at least one software testing parameter via the cloud native elastic tool;

perform within the cloud native elastic tool an automated software verification process comprising a regression algorithm on different branches of source code software of a source code repository hosting service to obtain at least one result based on the execute the at least one test suite; and

output electronically via a graphical user interface a report for the at least one test suite via the cloud native elastic tool, the report including information that corresponds to the at least one software testing parameter, an execution status, and the at least one result.

11 . The computing device of claim 10 , wherein the processor is further configured to:

detect the at least one testing error that corresponds to the execute of the at least one test suite, the at least one testing error including a testing failure due to the instabilities of the computing environment; and

automatically initiate via the cloud native elastic tool an action to re-execute the at least one test suite based on an analysis of the at least one testing error.

12 . The computing device of claim 11 , wherein the processor is further configured to:

provide at least one alert when the at least one testing error is detected, the at least one alert including information that relates to the at least one testing error, the automatically initiated action, and a determined origin of the at least one testing error; and

notify at least one responsible user with the at least one alert.

13 . The computing device of claim 10 , wherein the at least one test configuration defines at least one dependency and at least one testing sequence priority for each of a plurality of test cases that corresponds to the at least one test suite, each of the plurality of test cases including at least one predefined input and at least one predefined, expected output.

14 . The computing device of claim 10 , wherein the at least one software testing parameter facilitates execution of the at least one test suite on at least one from among a cloud computing environment and an on-premise computing environment, and

wherein the facilitation including adjustment of at least one dependency and at least one testing sequence priority for each of a plurality of test cases that corresponds to the at least one test suite.

15 . The computing device of claim 10 , wherein the processor is further configured to:

determine, by the least one ML model, at least one predicted dependency for each of a plurality of test cases that corresponds to the at least one test suite;

determine, by the at least one ML model, at least one predicted outcome for each of the plurality of test cases; and

determine, by the at least one ML model, at least one potential point of failure and a corresponding cause for each of the plurality of test cases.

16 . The computing device of claim 10 , wherein the error ratings comprise at least one among from a least square error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate.

17 . The computing device of claim 10 , wherein the processor is further configured to execute the at least one test suite based on a predetermined schedule that is defined in the at least one test configuration, the predetermined schedule including at least one from among a continuous testing schedule and an intermittent testing schedule.

18 . The computing device of claim 10 , wherein to perform the automated software verification to obtain the at least one result, the processor is further configured to:

automatically compare each of the at least one result with a corresponding output of a secondary execution of the at least one test suite, the secondary execution relating to secondary testing of the at least one test suite from a different branch of a source code repository hosting service;

automatically determine based on the regression and a validation analysis automation via the cloud native elastic tool, a verified status for each of the at least one result based on an outcome of the comparing, the verified status including at least one from among a passed status and a failed status; and

automatically mark each of the at least one result with the corresponding verified status.

19 . A non-transitory computer readable storage medium storing instructions for providing test and regression automation via a cloud native elastic tool, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

obtain from a module comprising an artificial intelligence (AI) module at least one testing condition from at least one test configuration, the at least one test configuration corresponding to at least one test suite;

implement at least one machine learning (ML) model via the cloud native elastic tool operating in a computing environment that comprises a distributed computing environment;

train the at least one ML model based on a cross-validation technique and a holdout technique in operating within a predetermined range of error ratings that corresponds to at least one software testing parameter based on the at least one testing condition associated with at least one testing error involving instabilities of the computing environment comprising a cluster instability and an infrastructure instability;

execute the at least one test suite based on the at least one software testing parameter via the cloud native elastic tool;

perform within the cloud native elastic tool an automated software verification process comprising a regression algorithm on different branches of source code software of a source code repository hosting service to obtain at least one result based on the executing the at least one test suite; and

output electronically via a graphical user interface a report for the at least one test suite via the cloud native elastic tool, the report including information that corresponds to the at least one software testing parameter, an execution status, and the at least one result.

20 . The storage medium of claim 19 , wherein the executable code which, when executed by the processor, further causes the processor to:

detect the at least one testing error that corresponds to the execute of the at least one test suite, the at least one testing error including a testing failure due to the instabilities of the computing environment; and

automatically initiate via the cloud native elastic tool an action to re-execute the at least one test suite based on an analysis of the at least one testing error.