Published Release Notes

Changes in large data page support in offline-enabled custom mobile apps

You can now create offline-enabled custom mobile apps that store reference data in large data pages that are sourced from report definitions, even if these report definitions return no records. Synchronization of these custom mobile apps does not fail. Also, you can now use page type properties as large data page properties.

For more information, see Creating and sourcing large data pages to store large reference data in offline-enabled applications and Custom populator JavaScript functions for large data pages.

Pega unit test suites are now modular

It is now possible to include test cases and test suites inside test suites. This feature makes test suites modular and improves the management of cases while creating, modifying, and executing them for regression testing.

For more information, see Pega unit test suites.

Troubleshooting offline-enabled applications in a browser

You can now troubleshoot offline-enabled applications by using the Tracer tool and developer tools that are available in a web browser. You can troubleshoot scripts that are running when the application is running, without having to use additional debugging tools.

For more information, see Application debugging using the Tracer tool, Troubleshooting offline-enabled applications with the Tracer tool in a browser, and Offline capability.

@java and locatable pages no longer supported

@java and locatable pages are no longer supported. Existing rules that use @java and locatable pages will continue to work; however, changes to these rules cannot be saved unless @java and locatable pages are replaced by supported functionality. Use a rule utility function (RUF) instead of an @java page, and use a data page instead of a locatable page. In addition, Pega Platform™ displays a guardrail warning when you open a rule that uses an @java page or a locatable page.

REST API now fetches the latest version of Application Quality metrics

When you request Application Quality metrics by using the REST service, the service requests a refresh of the metrics. This means that you always get the latest version of the metrics and can decide whether your application is ready for deployment based on that information.

For more information, see Pega API.

Parameter page is automatically removed after each Pega unit test run

The parameter page is automatically removed after each Pega unit test run. The reliability of subsequent tests is increased because they are not influenced by leftover parameter values that were set by the previous test.

For more information, see Cleaning up your test environment.

Start and stop a coverage session by using the REST API

You can now start and stop application-level test coverage by using REST services. You can use the results of the coverage session for gating purposes in a release pipeline.

For more information, see Pega API.

Search indexes require rebuilding after upgrading from Pega Platform 7.1.7 through 7.2.1

Pega Platform™ now supports Elasticsearch 5.6.9. If you are upgrading from Pega 7.1.7 through 7.2.1, discard your existing indexes and build new indexes in a new empty index directory. Create the search indexes by using the Search landing page or the full text indexer. If you are upgrading from Pega Platform 7.2.2 or later, no action is required. You can continue to use your existing search indexes.

For more information, see Rebuilding search indexes and Batch creation and updating of Elasticsearch index files from the command line.

Improved performance of decision strategies

Strategy rule performance has been improved through the implementation of a new engine. You can perform single and batch test runs to analyze strategy performance, locate and prevent potential issues, and optimize strategy components. Test runs now support data sets and data flows with multiple key properties. The redesigned Test run panel improves the display of information and highlights the most immediately relevant details.

For more information, see Configuring a single case runs and Configuring a batch case runs.

Extract summaries from the analyzed text

You can now configure a Text Analyzer rule to extract information-rich blocks of text from the analyzed content and combine them into a comprehensive and coherent summary. By summarizing large documents, such as emails, you can facilitate making business decisions without having to read an entire document. In Text Analyzer rules, you can combine summarization with other types of text analysis, such as topic or entity detection, to extract the full context from a message.

For more information, see Configuring text extraction analysis and Tutorial: Extracting email context with Text Analyzer rules.