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Test Execution Specialist Guide
This guide describes tasks that you must complete before you can configure and run tests in HCL DevOps Test Hub (Test Hub). You can find information about configuring runs for the different test types that are supported. You can also find information about other tasks that you can perform on the Resource Monitoring Service, Virtualization, and Integrations with third-party applications. This guide is intended for testers or test execution specialists.
Machine learning capabilities in DevOps Test Hub
HCL DevOps Test Hub (Test Hub) uses Machine learning (ML) algorithms to analyze tests that match certain requirements and criteria, and then presents the findings as insights and recommendations after the tests are run. You can use the insights and recommendations to interpret the test results and identify the problem areas in the system under test.
Rerunning Machine learning (ML) analysis
If you change the decision threshold value set for a parameter analyzer, you can then rerun the analyzer to analyze the parameter for the changed threshold and present the insight without running the tests again.

Test Execution Specialist Guide
This guide describes tasks that you must complete before you can configure and run tests in HCL DevOps Test Hub (Test Hub). You can find information about configuring runs for the different test types that are supported. You can also find information about other tasks that you can perform on the Resource Monitoring Service, Virtualization, and Integrations with third-party applications. This guide is intended for testers or test execution specialists.
- System modeling
  You can use the system model feature on HCL DevOps Test Hub (Test Hub) to create a model that simulates an application or system under test. You can define components to represent the actual components in the system under test, define their relationships and then view the components in a system under test as a diagrammatic representation.
- Prerequisites to running tests
  Before you can run a test asset in a project on HCL DevOps Test Hub (Test Hub), you must read the information about the different tests assets. You can add a remote static agent, Docker host, or Kubernetes cluster to the project and then select it as an alternate location to run the test assets.
- Management of agents
  You can find information about managing performance agents or API agents that you install on remote computers and then configure the remote agents to register with HCL DevOps Test Hub (Test Hub). After the agents are registered, you can add them to your projects and then select them as locations to run tests or virtual services.
- Management of Docker hosts
  You can find information about the tasks that you can perform on remote Docker hosts. After you set up a remote Docker host, you can register the Docker host with HCL DevOps Test Hub (Test Hub). You must then add the registered remote Docker host to your project before you can run tests on the remote Docker host location.
- Management of remote Kubernetes clusters
  You can find information about the tasks that you can perform on remote Kubernetes clusters. After you set up a remote Kubernetes cluster, you can register the external cluster with a team space on HCL DevOps Test Hub (Test Hub). You must then add the remote cluster to a project before you run test assets or resources on the remote Kubernetes cluster.
- Management of mobile device cloud credentials
  You can find information about managing the credentials that are required to connect to mobile device clouds from HCL DevOps Test Hub (Test Hub).
- Test run configurations
  You can configure and run tests in HCL DevOps Test Hub (Test Hub) after you add the test resources to your project in your team space.
- Configuration of test runs in a GitHub Actions Workflow
  You can configure test runs of test resources that are in a project repository of HCL DevOps Test Hub (Test Hub) in a GitHub Actions Workflow. You can choose to run test resources that are in your server project in the GitHub Workflow when you do not want to run the test resources without opening the Test Hub project.
- Configuration of a scan of an application code
  You can use HCL DevOps Test Hub (Test Hub) to scan your application code for security vulnerabilities similar to using the HCL® AppScan CodeSweep plugin to scan code in Visual Studio.
- Management of running tests
  Find information about the tasks that you can perform on a test that you configured for a run either while it runs or after it completes the run.
- Management of virtualized services
  You can find information about the tasks that you can perform on and manage the virtualized services that run on HCL DevOps Test Hub (Test Hub). You can start or stop the virtual services that are available to Test Hub. You can view the routing rules and usage statistics of virtualized services, agents, or intercepts that are connected to Test Hub.
- Test results
  After the tests, Suites, or schedules are run and completed, you can view the results and open the reports in HCL DevOps Test Hub (Test Hub) to analyze the verdict, the performance, and statistics. You can also re-execute tests and schedules from the Results page with the same commit id.
- Machine learning capabilities in DevOps Test Hub
  HCL DevOps Test Hub (Test Hub) uses Machine learning (ML) algorithms to analyze tests that match certain requirements and criteria, and then presents the findings as insights and recommendations after the tests are run. You can use the insights and recommendations to interpret the test results and identify the problem areas in the system under test.
  - Viewing Machine learning insights
    When you want to use Machine learning (ML) capabilities in HCL DevOps Test Hub (Test Hub), you need to run Performance Tests or Schedules that conform to certain criteria. After the test run is completed, you can view the ML insights on the Insights page in your project.
  - Interpretation of insights
    You can find information about interpreting the Machine learning (ML) insights into the supported parameters that are analyzed by HCL DevOps Test Hub (Test Hub) after certain tests are run.
  - Viewing Machine learning analyzers
    HCL DevOps Test Hub (Test Hub) analyzes certain types of tests that are run in a team space by using its Machine learning (ML) capabilities. You can view the list of analysers and description about the analyzers available in a Team space.
  - Interpretation of parameter analysis
    You can find information about interpreting the details of each of the Machine Learning (ML) analyzers that analyze the parameters after certain tests are run in HCL DevOps Test Hub (Test Hub).
  - Viewing reactions marked by members to Machine learning insights
    You can view the reactions that members of a project mark to show their agreement or disagreement with the insights into the parameters on HCL DevOps Test Hub (Test Hub).
  - Modifying Machine learning analyzer settings
    When you want to change the decision threshold set for a Machine learning (ML) analyzer or disable the analyzer, you can modify the settings from the Analyzer page.
  - Deleting Machine learning insights
    After you view the Machine learning (ML) insights into the tests, you might want to delete the insight if many members of your project disagree with the insight.
  - Rerunning Machine learning (ML) analysis
    If you change the decision threshold value set for a parameter analyzer, you can then rerun the analyzer to analyze the parameter for the changed threshold and present the insight without running the tests again.
- Resource monitoring
  When you apply load to a system under test, the amount of resources consumed by your system is increasing. If the capacity of the resources does not match the load, you can see performance issues in the results. Resource monitoring in HCL DevOps Test Hub (Test Hub) helps you monitoring the resources of a system and establish the performance metrics and usage data of the system. Thus, you can observe the health of these resources while a schedule is running.
- Configuration of a change management
  When you use any application to create and track defects (bugs), issues, or other work items, you can configure the application as a change management on HCL DevOps Test Hub (Test Hub). You can then create defects (bugs), issues, or other work items without the need to open your application.
- Integrations with other applications
  You can integrate certain applications with HCL DevOps Test Hub (Test Hub) to run tests, view the test results, and create defects.
- Managing access to DevOps Test Hub
  Desktop clients and third-party integrations use offline user tokens to connect to HCL DevOps Test Hub (Test Hub).
- Working with DevOps Test Hub REST services
  When you want to use the REST APIs of HCL DevOps Test Hub (Test Hub) in your test automation scripts, you can view the REST APIs that are exposed by Test Hub. You can also run the APIs from within your project in Test Hub.

Rerunning Machine learning (ML) analysis

If you change the decision threshold value set for a parameter analyzer, you can then rerun the analyzer to analyze the parameter for the changed threshold and present the insight without running the tests again.

Procedure

Log in to Test Hub.

The team space that contains your project is displayed.
Click Active projects > My projects > project_name to open the project that contains the test assets.

The Overview page of the project is displayed.
Click Analyze > Results from the navigation pane in your project.

The Results page is displayed.
Click the SUITES tab, if you ran a Schedule or the TESTS tab, if you ran a Performance Test.
Identify the test result for which you want to rerun the analysis by performing any of the following actions:
- Search for the test result by entering any text contained in the test asset name in the Search field.
- Sort the items in the table of results by clicking the Started by column header, and then identifying the test runs that you started.
Click the Open action menu icon from the Actions column that is in the row of the identified test result.
Click Rerun analysis .

The parameter is analyzed again with the changed threshold vale and the insight presented.

Results

You started the ML analysis of the previously analyzed test result so that the insight can be displayed based on the changed threshold value without running the test again.