Quality assurance · Production
OpenText Core Performance Engineering: cloud-native load testing with predictive analytics
The problem
Development teams face performance bottlenecks and complex test setup that slow releases and prevent early-cycle testing.
Workflow diagram · grounded in source
1
Design and launch 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
ai_action
“Spot anomalies early, pinpoint bottlenecks, and understand how your app behaves under any load”
5
Smart reports and dashboards
output
“Get instant insights with smart reports and dashboards”
Reported outcome
One anonymous user reports saving at least 30% in performance testing time, and the platform is described as best in class for monitoring, analytics, and ease of use.
Reported metrics
Performance testing time savingsat least 30%
Reported stack
OpenText Core Performance EngineeringAmazon Web ServicesAzureGoogle Cloud Platform
Frequently asked questions
What did this team achieve with this AI workflow?
One anonymous user reports saving at least 30% in performance testing time, and the platform is described as best in class for monitoring, analytics, and ease of use.
What tools did this team use?
OpenText Core Performance Engineering, Amazon Web Services, Azure, Google Cloud Platform.
What results were reported?
Performance testing time savings: at least 30% (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
Design and launch load test → Distribute traffic across cloud regions → Real-time metrics and predictive analytics → Anomaly and bottleneck detection → Smart reports and dashboards.