Systems | Development | Analytics | API | Testing

SeaLights for ABAP: Mapping SAP Risk and Closing Test Gaps

Pair SAP change tracking with automatic test mapping to know exactly what a transport touches, and what still needs coverage. In this walkthrough, Kateryna Serdinsky, Product Marketing Manager at Tricentis, demonstrates how to set up a test cycle, run risk-based impact analysis, and close coverage gaps in SeaLights for ABAP.

AI Workspace + Playwright: Deploy Playwright tests from Jira requirements

Connect AI Workspace, the Tricentis Quality Agent, and Playwright so a Jira requirement generates test cases and runs them as an unattended web test. In this walkthrough, you will see how a ticket drives test case generation, triggers a Playwright run over MCP, and returns a pass/fail report to Jira. Key highlights.

Enterprise Performance Testing closes the performance gap in SAP transformations

Quick summary: SAP Enterprise Performance Testing (EPT) by Tricentis now integrates with SAP Cloud ALM, so teams can create, run, and track performance tests in the same hub as the rest of their S/4HANA transformation. For teams managing SAP transformations, most major workstreams are visible in Cloud ALM, but performance testing has long been an exception. It has lived in a separate tool, forcing teams to treat it as a separate workflow that’s often left to the end of a project.

How IBM tests AI apps and code with AIDA and AI Workspace

Mike Dillon is a Global Platform Leader at IBM. While deeply technical and experienced in enterprise architecture, he is not a traditional hands-on software developer. Yet this spring, Dillon paired his domain expertise with IBM Bob, IBM’s AI coding assistant, to engineer his first full-scale enterprise application: WatsonAuditX. Designed to automate the documentation required for complex SAP audits, the app addresses a familiar operational roadblock.

What quality engineering has always been missing

In September, Anthropic’s CEO argued that AI companies should deliberately slow the pace at which they improve their models, because advances are outrunning researchers’ ability to understand and control them. Sam Altman endorsed the idea within hours, and other leaders of frontier AI labs quickly echoed him. For QE teams, the pace problem is already here. AI-generated change is arriving faster than anyone can review, and it isn’t arriving clean.

Tricentis Data Integrity: The one question your data quality tools can't answer.

Your data quality tools check data inside their scope. But can they tell you whether data is still right after it moves across three systems and five transformations? In this video, Adnan from Tricentis explains the gap that data quality tools were never designed to fill. Data moves from your ERP to your data warehouse, to your reporting layer, and from one cloud to another, and every boundary it crosses is a chance for something to change. Learn how end-to-end testing and data reconciliation complement your existing tools by testing data from source to target.

What five QE orgs got right about AI transformation

Not all AI transformations are equal, but when AI works well in quality engineering, the results are hard to ignore: 65% fewer major incidents, AI-generated test volume growing 6-7× in a year, or a five-step manual process replaced by three automated workflows. These are just a few of the outcomes from companies that moved past scattered AI experimentation to quality engineering that runs on a continuous, organization-wide signal.

What's new in Tricentis qTest: August 2026

See a generated test plan before it runs, and push a manual test case straight into automated execution across qTest and Tosca Cloud. In this walkthrough, Kateryna tours the refreshed qTest interface, previews an AI-generated test plan before it spends any agentic credits, and shows how to convert a manual test case into an automated Tosca Cloud run. Key highlights.

Tricentis Data Integrity: Validate your data. Trust every decision.

Most business decisions rest on data — but is that data actually tested? Not monitored, not profiled, but verified as fit for business use? For most organizations, the honest answer is no. In this video, Adnan walks through why data testing is a business problem, not just a data problem, and how Tricentis Data Integrity automates data validation end-to-end so you can trust every decision you make.

Why testing Oracle Fusion Agentic Applications is different - and how QA teams can adapt

Oracle’s Fusion Agentic Applications represent a new class of enterprise software. They contain coordinated teams of outcome-driven, proactive AI agents that are engineered to execute business processes across finance, HR, supply chain, and customer experience. For folks who work in those departments, these agents may streamline some of their daily work and help uplevel employees. For testers, they bring a measure of ambiguity. In general, you know how humans use these applications.