Published Release Notes

Run an application test suite from a CI/CD pipeline

Deployment Manager, as well as the existing REST service used to call scenario tests, now accepts a test suite ID when running scenario tests from an application pipeline. By using test suites, you can customize a set of select scenario tests to run as a group, instead of running all the scenario tests that are applied to a particular application.

Only the tests that belong to the suite are run as part of the task. 

For more information, see Creating UI-based tests with scenario testing. 

Access PegaUnit compliance metrics from a centralized location

PegaUnit compliance metrics and execution rate have been added to the PegaUnit metrics tile of the Application Quality dashboard. This dashboard provides a centralized location for all PegaUnit data for a specific application. 

The dashboard also supports granular PegaUnit test information for each case type and data type, similar to the process currently available on the branch quality dashboard. 

For more information, see Analyzing application quality metrics. 

Run PegaUnit tests by using an API call

A new REST API is now available to create and run PegaUnit tests from an external continuous improvement (CI) tool. This synchronous method allows for the processing of large quantities of tests while reducing the possibility of time-out issues. This additional method of performing PegaUnit testing does not impact users who want to continue using their current testing workflow. 

For more information about setting up your environment and making API calls with Deployment Manager, see the Documentation/readme-for-swagger.md file in the DeploymentManager04_08_0x.zip file, found in the Deployment Manager download package. 

Merge application-level test reports by using the Pega API service

A new REST API is now available so that you can merge test coverage reports at the application level without having to perform this task manually. This service accepts the application IDs for the test coverage reports that you want to merge and generates a consolidated report in the target application provided.

For more information about setting up your environment and making API calls with Deployment Manager, see the Documentation/readme-for-swagger.md file in the DeploymentManager04_08_0x.zip file, found in the Deployment Manager download package.

Improvements to scenario testing for increased efficiency

Several enhancements have been made to scenario tests for increased test efficiency. See below for a list of these improvements.

• Scenario testing now supports multiple actions per run, and all events that are configured as part of a scenario test are now triggered sequentially. Previously, only the first event was triggered, and the system ignored the rest. 
• If a scenario test encounters a JavaScript error at any point during the run, the system immediately stops the tests. Refer to the logs for a detailed description of the error. 
• Actions can now be configured and recorded on custom components in the scenario testing tool. 

For more information, see Creating UI-based tests with scenario testing. 

Uploading customer responses into adaptive models is no longer available

The option to train adaptive models by uploading a static list of historical interaction records has been deprecated in Pega Platform™ 8.5.

Upgrade impact

In versions of Pega Platform earlier than 8.5, it was possible to train an adaptive model by uploading historical data of customer interaction. After the upgrade to version 8.5, this option is no longer available.

What steps are required to update the application to be compatible with this change?

Use data from a report definition to train adaptive models. For more information, see Training adaptive models.

More flexibility for Date Time predictors in adaptive models

You can configure adaptive model predictors that indicate the amount of time that has elapsed since a particular event. In versions of Pega Platform™ earlier than 8.5, you could only specify the absolute date and time, for example, the date when a subscriber last visited one of your brick-and-mortar store locations. In Pega Platform 8.5, you can also indicate the amount of time that has passed, for example, the number of days since a subscriber purchased a new service plan.

For more information, see Adding a predictor to an adaptive model.

Predictive models can drive predictions

You can use predictive models as the basis of your predictions. As a data scientist, you can now use Machine Learning Operations (MLOps) to replace models in your system. You can replace a model in a prediction with a PMML, H2O MOJO, or Pega OXL predictive model, as well as a scorecard or field, and then approve the update for deployment to a production environment. You can respond to a Prediction Studio notification that an active model does not generate enough lift and decide to replace the low-performing model with a high-accuracy model. You can also update a prediction on a regular basis, for example, whenever you develop a new churn model in an external environment.

For more information, see Replace models in predictions and migrate changes to production with MLOps.

Enhancing the performance of your Next-Best-Action strategy with globally optimized strategies

Starting in version 8.6, Pega Platform™ combines the versatility of Next-Best-Action Designer with strategy performance enhancements provided by using globally optimized strategies (GOS). Decrease the run time and memory usage of executing the Next-Best-Action strategy in batch or real-time scenarios by using the globally optimized strategies generated by Next Best Action Designer.

GOS is supported by Pega Platform's standard business change management process. GOS rules are automatically included in relevant revision packages.

For more information, see Enhance the performance of your Next-Best-Action strategy with globally optimized strategies (8.6).

Support for picklists with parameterized data pages in App Studio in Cosmos React UI

You can now use data pages with parameters to populate a property of the picklist type with filtered results in App Studio. For example, in a survey case type, you can use a parameterized data page to configure cascading drop-down controls in which the values in a secondary drop-down list are based on the value that the user selects in the primary drop-down list. With dynamically-sourced picklists, you get greater flexibility in configuring picklists, and users see more accurate values.

For more information, see link Dropdown control Properties — General tab.