Quality assurance · Production
OpenText Core Performance Engineering: cloud-native load testing with predictive analytics and anomaly detection
The problem
Teams needed a way to test application performance under real-world load without complex setup, and to detect bottlenecks early enough to avoid production issues and ensure stability under peak traffic.
Workflow diagram · grounded in source
1
Design and run load test
trigger
“Design and run load tests in minutes—no complex setup required. Empower developers, QA, and PMs to test performance early and often with a cloud-native solution.”
2
Distribute traffic across cloud regions
integration
“Distribute traffic across 40+ cloud regions with Amazon Web Services, Microsoft® Azure, Google Cloud Platform™, or run hybrid tests with on-premises load generators.”
3
Real-time metrics and predictive analytics
ai_action
“Get instant visibility into performance with real-time metrics and predictive analytics.”
4
Anomaly and bottleneck detection
validation
“Spot anomalies early, pinpoint bottlenecks, and understand how your app behaves under any load.”
5
Shift-left issue remediation
feedback_loop
“Detect and fix issues early with shift-left testing for smoother app performance and customer satisfaction.”
Reported outcome
A customer reports saving at least 30% in performance testing time with OpenText Core Performance Engineering.
Reported metrics
Performance testing time savedat least 30%
Reported stack
OpenText Core Performance EngineeringAmazon Web ServicesMicrosoft® AzureGoogle Cloud Platform™
Frequently asked questions
What did this team achieve with this AI workflow?
A customer reports saving at least 30% in performance testing time with OpenText Core Performance Engineering.
What tools did this team use?
OpenText Core Performance Engineering, Amazon Web Services, Microsoft® Azure, Google Cloud Platform™.
What results were reported?
Performance testing time saved: at least 30% (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
Design and run load test → Distribute traffic across cloud regions → Real-time metrics and predictive analytics → Anomaly and bottleneck detection → Shift-left issue remediation.