Pega is one of the most powerful low-code platforms used by enterprises across banking, healthcare, insurance, telecommunications, and government. It makes building and evolving complex applications fast and easy. But that same speed and flexibility makes testing on Pega a serious challenge.
This whitepaper examines why traditional test automation tools break down on Pega, and how XPeer.AI solves this through AI-native, no-code automation built for Pega's dynamic nature.
Inside This Whitepaper
- Pega's UI changes dynamically during real-time interactions, with centre-out workflows, branching paths, and role-based behavior.
- Frequent citizen developer changes and bi-yearly platform updates make static test suites obsolete quickly.
- High maintenance costs, slow delivery, and quality bottlenecks are common despite significant QA investment.
- In-house automation tools often offer limited coverage and require immense expertise to maintain.
What Makes XPeer.ai Different
- Test cases are intelligent and self-healing, they learn from Pega's workflows and UI changes and update automatically
- Complete coverage across unit, functional, end-to-end, regression, and integration testing, all mapped to user stories, epics, and features
- No coding expertise required, citizen developers, QAs, developers, and business analysts all work on the same platform, just like Pega's own no-code ideology
- Test data is generated automatically across integrations, aligned with original datasets and compliance requirements
- Live analytical reports support faster, more confident release decisions
Download the Full Whitepaper
Access the complete capability mapping and implementation strategy for AI-native Pega test automation.
XPeer.ai | info@icodetest.com | Hyderabad, India
Statistics cited are drawn from published research including Johns Hopkins Patient Safety data, CMS regulatory guidance, and XPeer.ai customer outcomes. Individual results may vary based on organization size, technology environment, and implementation scope.
Return to resources pageStatistics cited are drawn from published research including Johns Hopkins Patient Safety data, CMS regulatory guidance, and XPeer.ai customer outcomes. Individual results may vary based on organization size, technology environment, and implementation scope.