Welcome
Test Execution Specialist Guide
This guide describes tasks that you must complete before you can configure and run tests in HCL OneTest™ Server. 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 HCL OneTest™ Server
HCL OneTest™ Server uses Machine learning (ML) algorithms to analyze test results 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.
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.

Release Notes
This document includes information about What’s new, installation and upgrade instructions, known issues, and contact information of HCL Customer Support.
System Requirements
This document includes information about hardware and software requirements for HCL OneTest™ Server.
Getting Started Guide
This guide provides an overview of HCL OneTest™ Server. You can find the task flows to get you started with HCL OneTest Server. This guide is intended for new users.
Administrator Guide
This guide describes how to install the HCL OneTest™ Server software.
Test Author Guide
This guide describes how to create and edit test assets in HCL OneTest™ Server, and then publish test assets to the remote Git repository that you have configured in your project. This guide is intended for testers and test managers.
Test Execution Specialist Guide
This guide describes tasks that you must complete before you can configure and run tests in HCL OneTest™ Server. 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 OneTest™ Server 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 OneTest™ Server, 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 OneTest™ Server. 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 OneTest™ Server. 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 OneTest™ Server. 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 OneTest™ Server.
- Test run configurations
  You can configure and run tests in HCL OneTest™ Server 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 OneTest™ Server 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 HCL OneTest Server project.
- Configuration of a scan of an application code
  You can use HCL OneTest™ Server 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 OneTest™ Server. You can start or stop the virtual services that are available to HCL OneTest Server. You can view the routing rules and usage statistics of virtualized services, agents, or intercepts that are connected to HCL OneTest Server.
- Test results
  After the tests, Suites, or schedules are run and completed, you can view the results and open the reports in HCL OneTest™ Server to analyze the verdict, the performance, and statistics. You can also re-execute tests and schedules from the Result page with the same commit id.
- Machine learning capabilities in HCL OneTest™ Server
  HCL OneTest™ Server uses Machine learning (ML) algorithms to analyze test results 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 OneTest™ Server, 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 OneTest™ Server after certain tests are run.
  - Viewing Machine Learning analyzers
    HCL OneTest™ Server analyzes certain types of tests after they 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 when certain tests are run in HCL OneTest™ Server.
  - Viewing reactions marked by members to Machine Learning (ML) insights
    You can view the reactions that members of a project mark to show their agreement or disagreement with the insights into the parameters.
  - 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 OneTest™ Server 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 system
  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 system on HCL OneTest™ Server. 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 OneTest™ Server to run tests, view the test results, and create defects.
- Managing access to HCL OneTest™ Server
  Desktop clients and third-party integrations use offline user tokens to connect to HCL OneTest™ Server.
- Working with HCL OneTest™ Server REST services
  When you want to use the HCL OneTest™ Server REST APIs in your test automation scripts, you can view the REST APIs that are exposed by HCL OneTest Server. You can also run the APIs from within your project in HCL OneTest Server.
Test Manager Guide
This guide describes how to manage and track your overall test effort. This guide is intended for test managers.
Troubleshooting
This guide describes how to analyze and resolve some of the common problems that you might encounter while you work with HCL OneTest™ Server.
All Guides (PDF)
You can find the link to view or download the content of the release notes, system requirements, and persona guides of HCL OneTest™ Server as a PDF file.
Security Considerations
This document describes the actions that you can take to ensure that your installation is secure, customize your security settings, and set up user access controls.
Notices
This document provides information about copyright, trademarks, terms and conditions for the product documentation.

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.

Before you begin

You must have completed the following tasks:

Ensured that you are assigned a role as a Viewer, Tester, or Project Owner in the project.
Viewed the insights into tests provided by ML analysis. See Viewing Machine Learning insights.
Viewed the reactions from members of your project to the ML insight. See Viewing reactions marked by members to Machine Learning (ML) insights.

Procedure

Log in to HCL OneTest™ Server.

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.
Perform any of the following actions to view and identify the insight:
- Scroll down the Overview page to see the Insights section.
  The Insights section on the Overview page displays a table of the insights for the supported parameters identified from the tests that were run in the project. You can identify the insight and click any item in the row of the identified insight to display the insight on the Insights page.
- Click Analyze > Insights in the navigation pane. The Insights page is displayed. You can identify the insight that you want to delete.
Click the Delete icon from the Actions column that is in the row of the identified insight.

Results

You deleted an insight into the test arrived at by ML analysis of the test.

What to do next

You can perform any of the following tasks:

Run tests to let the ML analyzers reanalyze and present the insights. See Test run configurations.
Rerun the analysis without running the tests again so that the deleted instance of the insight is redisplayed. See Rerunning Machine Learning (ML) analysis.