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Regression Test Case Selection Using Machine Learning

The best software testing method for retesting software is regression testing. When new features or updates and modifications in code are done, regression testing is proved to be a worthwhile testing method. In some scenarios, the whole program cannot be retested as it is not feasible given the amount of cost and time. To overcome this problem, a subset of all test cases is executed and then these test cases are executed based on test case prioritization. In this article, you will know about regression test case selection using machine learning.

Test case selection can be carried out through a vast amount of methods, but, mostly it is based on the domain expertise of Subject Matter Expert / Test Engineers. The Manual process is iterative, time-consuming and the engineer’s skills largely depend on it, which means the chances of missing some relevant test cases are high. In this article, a Proof of Concept (POC) is used to select manual regression test cases.

Preparing and collecting data: Authorization microservice test cases are considered across the four release cycles pertaining to the test data. Oauth2 standard is what the microservice is based on, which is widely used in industry for the purpose of authorization across microservices/systems.

Following are four key steps that depict the happening of the test selection process:

1. Release Manifest: Versioned stuff is a collection of issues/stories/artifacts, description and configuration settings that are going to be deployed in a particular phase.

2. JIRA: It is important to carry out agile project management and Bug tracking tool

3. Service Functionality Mapping File: It is a matrix that consists of mapping between functionality and microservices. The impacted area can be better understood by users when a particular microservice gets affected.

4. Test Rail: It is a Test Management Tool.

 The above mentioned four key steps can be better understood by the five-step process mentioned below:

1. Step 1: Release Manifest will be referred by Subject Matter Expert (SME) for every release to understand the microservices that are under test and in that particular release, the details of the commits/fixes are also taken into consideration.

2. Step 2: Stories will be taken by SME and the Bug ID’s from Release Manifest and then JIRA will be navigated to get more relevant details that are based on the domain knowledge SME. The functionality that has been impacted is understood by the Service Functionality Mapping File that is being referred to.

3. Step 3: Based on the information gained from JIRA, the service functionality mapping file is again referred by SME to get the list of impacted functionality.

4. Step 4: From the above two steps, based on the impacted functionality list, Test Rail is navigated by SME to search relevant test cases

5. Step 5: With the data that has been collected and the domain knowledge that has been gained by above mentioned steps, the list of test cases will be selected by SME from Test Rail.                  

The above mentioned information provides only a partial understanding of using the platform of machine learning for the purpose of regression test case selection. There is a lot more that needs to be covered as the entire process is vast. If you are looking for a detailed and in-depth explanation about selecting regression test cases using a machine learning platform, then the online platform and research is one of the options available.                

Conclusion: If you are looking forward to implementing regression testing for your specific project, then do get connected with a premium software testing services company that will provide you with a feasible testing strategy that is in line with your project specific requirements.

About the author: I am a technical content writer focused on writing technology specific articles. I strive to provide well-researched information on the leading market savvy technologies. 

 

 

 

 

 

 

 

 

 

 

 

 

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