Utilizing artificial intelligence to test cloud applications

Patent metadata

Field	Value
Publication number	US-2019213115-A1
Application number	US-201815864610-A
Country	US
Kind code	A1
Filing date	Jan 8, 2018
Priority date	Jan 8, 2018
Publication date	Jul 11, 2019
Grant date	—

Abstract

A device receives application information associated with a cloud application provided in a cloud computing environment, and utilizes a first AI model to generate test cases and test data based on the application information. The device utilizes a second AI model to generate optimized test cases and optimized test data based on the test cases and the test data, and utilizes a third AI model to generate test classes based on the optimized test cases and the optimized test data. The device executes the test classes to generate results, and utilizes a fourth AI model to generate an analysis of the results, recommendations for the cloud application based on the analysis of the results, or a code coverage report associated with the cloud application. The device automatically causes an action to be performed based on the analysis of the results, the recommendations, or the code coverage report.

First claim

1 . A device, comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories, to:
receive application information associated with a cloud application provided in a cloud computing environment,
the application information including source code for the cloud application;

utilize a first artificial intelligence model to automatically generate test cases based on a static code analysis of the cloud application, control flow information derived from the source code for the cloud application, and patterns in test data;
utilize a second artificial intelligence model to generate optimized test cases and optimized test data based on the test cases and the test data;
utilize a third artificial intelligence model to generate test classes based on the optimized test cases and the optimized test data;
execute the test classes to generate results;
utilize a fourth artificial intelligence model to generate at least one of:
an analysis of the results,
one or more recommendations for the cloud application based on the analysis of the results, or
a code coverage report associated with the cloud application; and

automatically cause an action to be performed based on the at least one of the analysis of the results, the one or more recommendations, or the code coverage report,
the action being associated with the cloud application.

2 . The device of claim 1 , where the application information includes one or more of:
metadata associated with the cloud application, training data for the first artificial intelligence model, or defect data associated with the cloud application.

3 . The device of claim 1 , where each of the test cases includes one or more of:
a test case identifier, the test data, a test sequence, an expected result, an actual result, or status information.

4 . The device of claim 1 , where the one or more processors, when utilizing the second artificial intelligence model to generate the optimized test cases and the optimized test data, are to:
remove a first particular test case from the test cases when the first particular test case cannot be used for testing the cloud application; remove a second particular test case from the test cases when the second particular test case is a duplicate of another one of the tests cases; and remove a third particular test case from the test cases when the third particular test case provides a same coverage as another one of the test cases,
the test cases, with the first particular test case, the second particular test case, and the third particular test case removed, representing the optimized test cases.

5 . The device of claim 1 , where the one or more processors, when utilizing the third artificial intelligence model to generate the test classes, are to:
cluster the optimized test cases and the optimized test data to generate clustered test cases and clustered test data; and utilize the clustered test cases, the clustered test data, and the application information, to generate the test classes,
each test class including one or more of the clustered test cases.

6 . The device of claim 1 , where the one or more processors, when utilizing the fourth artificial intelligence model to generate the at least one of the analysis of the results, the one or more recommendations, or the code coverage report, are to:
compare the results with predicted results for the test classes; and generate the at least one of the analysis of the results, the one or more recommendations, or the code coverage report based on comparing the results with the predicted results.

7 . The device of claim 1 , where each of the first artificial intelligence model, the second artificial intelligence model, the third artificial intelligence model, and the fourth artificial intelligence model includes a machine learning model.

8 . A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
receive, from a user device, a request to test a cloud application provided in a cloud computing environment;
receive application information associated with the cloud application based on the request,
the application information including source code for the cloud application;

utilize a first machine learning model to automatically generate test cases based on a static code analysis of the cloud application, control flow information derived from the source code for the cloud application, and patterns in test data;
utilize a second machine learning model to generate optimized test cases based on the test cases;
utilize a third machine learning model to generate test classes based on the optimized test cases;
execute the test classes to generate results;
utilize a fourth machine learning model to generate an analysis of the results; and
automatically cause an action to be performed based on the analysis of the results,
the action being associated with the cloud application and including at least one of:
correcting an error in the cloud application,
providing a recommendation to correct the error in the cloud application, or
generating code to improve the cloud application.

9 . The non-transitory computer-readable medium of claim 8 , where the instructions further comprise:
one or more instructions that, when executed by the one or more processors, cause the one or more processors to: utilize the fourth machine learning model to generate:
one or more recommendations for the cloud application based on the analysis of the results, and
a code coverage report associated with the cloud application; and
provide the analysis of the results, the one or more recommendations, and the code coverage report for display to the user device.

10 . The non-transitory computer-readable medium of claim 8 , where the application information includes one or more of:
metadata associated with the cloud application, training data for the first machine learning model, or defect data associated with the cloud application.

11 . The non-transitory computer-readable medium of claim 8 , where the one or more instructions, that cause the one or more processors to utilize the second machine learning model to generate the optimized test cases, cause the one or more processors to:
remove particular test cases from the test cases when one of:
the particular test cases cannot be used for testing the cloud application,
the particular test cases are duplicates of other ones of the tests cases, or
the particular test cases provide a same coverage as the test cases,
the test cases, with the particular test cases removed, representing the optimized test cases.

12 . The non-transitory computer-readable medium of claim 8 , where the one or more instructions, that cause the one or more processors to utilize the third machine learning model to generate the test classes, cause the one or more processors to:
cluster the optimized test cases to generate clustered test cases; and utilize the clustered test cases and the application information to generate the test classes, each of the test classes including one or more of the clustered test cases.

13 . The non-transitory computer-readable medium of claim 8 , where the cloud application includes one of:
an information as a service (IaaS) cloud application, a platform as a service (PaaS) cloud application, or a software as a service (SaaS) cloud application.

14 . The non-transitory computer-readable medium of claim 8 , where the one or more

Assignees

• Accenture Global Solutions Ltd

Inventors

• Takawale Paresh
• Dhumal Dnyaneshwar Gangaram
• Gupta Garima
• Patidar Mukul Dilip

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F11/3684 Primary

  for test design, e.g. generating new test cases · CPC title

• G06F11/3676

  for coverage analysis · CPC title

• G06F11/3688

  for test execution, e.g. scheduling of test suites · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title