Using similarity analysis and machine learning techniques to manage test case information

Patent metadata

Field	Value
Publication number	US-10768893-B2
Application number	US-201715818456-A
Country	US
Kind code	B2
Filing date	Nov 20, 2017
Priority date	Nov 20, 2017
Publication date	Sep 8, 2020
Grant date	Sep 8, 2020

Abstract

A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.

First claim

What is claimed is:

1. A device comprising:
one or more processors; and
one or more instructions that, when executed by the one or more processors, cause the one or more processors to:
obtain test case information for a set of test cases from a data source, the test case information including at least one of test case description information or test case environment information;
determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information,
the test case information being sorted into a set of test case groups;

determine a set of overall similarity scores for the set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores,
each test case group of the set of test case groups being sorted into the set of test case field groups;

receive feedback information associated with at least a portion of the set of overall similarity scores;
modify one or more values associated with the machine learning technique based on the feedback information;
determine a new set of overall similarity scores for the set of test case groups by using the machine learning technique with the one or more modified values to analyze the set of test case field groups;
update a data structure that stores the test case information into the data source to establish one or more associations between the test case information and the new set of overall similarity scores; and
process a request from a user device using information included in the updated data structure,
the request being a request for one or more test cases and including parameters identifying characteristics of the one or more test cases by:
obtaining the one or more test cases by searching, based on the parameters, the updated data structure that stores the test case information for the one or more test case requested; and
providing the one or more test cases to the user device.

2. The device of claim 1 , where the test case information includes test case defect information.

3. The device of claim 1 ,
where each test case group includes test case information for two or more test cases, and
where the one or more processors, when determining the set of field-level similarity scores, are to:
analyze the set of test case field groups with the set of similarity analysis techniques, and
determine the set of field-level similarity scores based on analyzing the set of test case field groups with the set of similarity analysis techniques.

4. The device of claim 1 , where the set of similarity analysis techniques includes at least one of:
a tuple-based similarity analysis technique,
a data structure-driven similarity analysis technique,
an approximation-based similarity analysis technique, or
a reduction-based similarity analysis technique.

5. The device of claim 1 , where the one or more processors, when determining the set of overall similarity scores, are to:
determine an overall similarity score, of the set of overall similarity scores, for a test case group, of the set of test case groups, by using the machine learning technique to analyze field-level similarity scores associated with the test case group,
where the one or more processors, when using the machine learning technique to analyze the field-level similarity scores, are to:
determine an overall similarity score for test case field groups that include the test case description information,
determine an overall similarity score for test case field groups that include the test case environment information, and
determine the overall similarity score for the test case group based on the overall similarity score for the test case field groups that include the test case description information and the overall similarity score for the test case field groups that include the test case environment information.

6. The device of claim 1 , where the one or more processors are further to:
receive additional feedback information associated with at least a portion of the new set of overall similarity scores;
further modify the one or more values associated with the machine learning technique based on the additional feedback information; and
determine another new set of overall similarity scores for the set of test case groups by using the machine learning technique with the one or more further modified values to analyze the set of test case field groups,
where the one or more processors are to continue to receive additional feedback information, further modify the one or more values associated with the machine learning technique, and determine new sets of overall similarity scores until a threshold is satisfied.

7. The device of claim 1 , where the one or more processors, when processing the request, are to:
receive, after updating the data structure, the request from the user device,
the request being a request to consolidate the test case information;

compare the new set of overall similarity scores and a similarity threshold to identify one or more test case groups that satisfy the similarity threshold;
remove, from the data structure, test case information associated with the one or more test case groups that satisfy the similarity threshold; and
provide, to the user device, an indication that the test case information has been consolidated.

8. The device of claim 1 , where the one or more processors, when receiving the feedback information, are to:
receive negative feedback information associated with the at least the portion of the set of overall similarity scores; and
where the one or more processors, when modifying the one or more values associated with the machine learning technique, are to:
modify the one or more values associated with the machine learning technique based on the negative feedback information.

9. 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:
obtain test case information for a set of test cases from a data source,
the test case information for the set of test cases including at least one of:
test case description information,
test case environment information, or
test case defect information;

determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information,
the test case information being sorted into a set of test case groups;

determine a set of overall similarity scores for the set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores,
each test case group of the set of test case groups being sorted into the set of test case field groups;

receive feedback information associated with at least a portion of the set of overall similarity scores;
modify one or more values associated with the machine learning technique based on the feedback information;
determine a new set of overall similarity scores for the set of test case groups by using the machine learning technique with the one or more modified values to analyze the set of test case field groups;
update a data structure that stores the test case information into the data source to establish one or more associations between the test case information and the new set of overall similarity scores; and
process a request from a user device using information included in the updated data structure,
the request being a request for one or more test cases and including parameters identifying characteristics of the on

Assignees

• Accenture Global Solutions Ltd

Inventors

• Misra Janardan
• Rawat Divya
• DUBASH Neville
• Podder Sanjay

Classifications

• G06F11/263

  Generation of test inputs, e.g. test vectors, patterns or sequences {; with adaptation of the tested hardware for testability with external testers} · CPC title

• G06F11/3676

  for coverage analysis · CPC title

• G06F7/02 Primary

  Comparing digital values (G06F7/06, {G06F7/22,} G06F7/38 take precedence) · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F11/368

  for test version control, e.g. updating test cases to a new software version · CPC title