Published Release Notes

Improve application test coverage by running multiple sessions

You can improve the test coverage of your application by using a new test coverage method - Application Coverage. Multiple users can perform coverage sessions which are aggregated into a single report. By using report metrics that show the number of rules that are covered and are not covered by tests, developers can identify which areas of the application need more test coverage.

For more information, see the Test Coverage landing page.

Unit testing support for more rule types

You can now create unit tests for the following additional rule types. You can also create assertions to validate activity status. The expanded rule types for unit testing enable developers to more thoroughly perform regression testing of their application, thereby improving application quality.

• Collection
• Declare expression
• Map value
• Report definition

For more information about unit testing rules, see Pega unit test cases.

Upgrade impact

With the four new rule types, unit test execution and unit test compliance metrics will change. Reports on automated unit testing of the customer application decrease due to the increased pool of supported rules.

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

After a successful upgrade, create Pega unit test cases for the newly supported rules to see updated and accurate unit test metrics.

View application quality metrics by data type

You can now view application quality metrics by data type on the Application Quality landing page. The new Data Types tab displays metrics for data types grouped by data objects, which enables you to more quickly understand the overall coverage of the application's integrations and interfaces.

For more information about data type metrics, see Application Quality landing page.

Scenario tests are reusable

Existing scenario tests are now reusable in different business scenarios. Before Pega 8.3, you had to create a new test every time a user interface or process flow changed significantly. Now, scenario tests are editable to help you maintain the test stack more effectively.

For more information, see Updating scenario tests.

Support for the decisioning-enabled NLP text analyzer in IVAs and Email Bots

You can now use the new intelligent natural language processing (iNLP) text analyzer in Pega Intelligent Virtual Assistants™ (IVAs) and Pega Email Bots™. This advanced text analyzer is decisioning-enabled and empowers IVA or Email Bot for all conversational channels to provide smarter responses. The decisioning capability in the system integrates text analytics with strategies, prepositions, and interaction history, to provide the context for making better next-best-action decisions.

For more information, see Use the decisioning-enabled NLP text analyzer in IVAs and Email Bots, Defining advanced Facebook text analyzer configuration and Facebook channel behavior.

Upgrade impact

Existing channels remain with the current configuration, and their current functionality does not change. Channels created after the upgrade automatically use the new text analyzer.  

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

The new text analyzer is enabled or disabled using a new interactive API (iAPI) selection, which is selected by default for new channels. You can disable the iAPI for new channels if you want to maintain current functionality. You can also enable it for existing channels if you want to move existing channels to the new functionality.

Improvements for automated scenario testing

Test automation authors can group related scenario tests into suites. The scenario test suites can be run manually from the Scenario Testing landing page as part of purpose-specific tests such as smoke tests, regression tests, and outcome-based tests. Additionally, automation authors or release managers who monitor tests for an application can disable or quarantine unstable scenario tests so that they do not run.

For more information about creating and managing test suites for scenario testing, see Creating test suites for scenario testing.

Support for multiple work queues in the Email Manager portal

Pega Email Bot™ now provides multiple work queues in the Email Manager portal. With this functionality, operators, such as customer service representatives, can now triage cases more efficiently by selecting different work queues. Operators have access to work queues only for the current Pega Platform™ application.

For more information, see Email Manager portal.

Improvements for test cases and assertions

The process of modifying test cases and assertions has been improved. Adjusting test cases to application changes is now much easier.

You now can:

• Select a page on which to run a tested rule.
• Change the class and rule of unit test cases.
• Create assertions that validate specific error messages on pages, properties, and activities.
• Automatically update decision result assertions with property changes made to a rule.
• Modify a rule's properties directly from decision result assertions.

For more information, see:

• Updating scenario tests
• Setting up your test environment
• Configuring page assertions
• Configuring property assertions
• Configuring decision result assertions

Automatic model training when mapping entities to case properties in Email Bot

You can now initiate automatic feedback to entity models in Pega Email Bot™, during manual mapping of email content to a case property.

To enable automatic feedback, you set the Work-Channel-Triage.pyIsRuntimeFeedback rule to true in Pega Platform™. By default this feature is disabled. Enabling this feature ensures that the email bot is more responsive by automatically copying detected entities, such as names, locations, dates, and ZIP codes, to case type properties of a case type.

For more information, see Email triage, Email channel NLP model and Enabling the NLP model training for the email channel.

Email bots now support multiple languages

You can now configure Pega Email Bot™ to perform text analysis, use training data and triage emails in multiple languages, for example, English, French, German, and Spanish. By detecting topics and entities from emails in different languages, the system can suggest the correct business case and provide an email response in the user's own language.

For more information, see Selecting languages for an Email channel and Enabling automatic language detection for text analysis.