The PAC aims to promote engagement between various experts from around the world, to create relevant, value-added content sharing between members. For Neotys, to strengthen our position as a thought leader in load & performance testing. Since its beginning, the PAC is designed to connect performance experts during a single event. In June, during 24 hours, 20 participants convened exploring several topics on the minds of today’s performance tester such as DevOps, Shift Left/Right, Test Automation, Blockchain and Artificial Intelligence.

1. Continuous Performance Innovative Approach
Bruno Da Silva

2. Performance 101

3. Performance101
Performance drivers :
• application
✓functionalities, design patterns, frameworks,
backend usage…
• resources
✓clustering, virtualization, network capacity,
shared components…
• load
✓use cases, user profiles, load peaks, new app vs
existing app…
How is Continuous Performance relevant ?

4. Performancetestingcategories
1. Continuous Performance testing : Robustness of the code
• Unit testing
• Component testing
2. Traditional Performance testing : Architecture stability
• Soak testing
• Stress testing
3. Advanced Performance testing : Real-world experience
• User experience testing
• Failover testing
Method depends on context : risks to mitigate, new vs existing app…

5. Continuous Performance challenges

6. Continuous Performancebenefits
• Reducing costs
• Mitigating risks by addressing performance
early
• Adapting architecture to the context
• Testing early to improve code
• Automating for regression detection
• Tuning monitoring before production
• Building performance together
• Anticipating for traditional and advanced
performance testing : Tests always up-to-
date

7. Continuous Performanceprerequisites
• Defining performance requirements earlier :
time, concurrency, capacity…
• Testable software
• Production-like environment
• Operational delivery pipeline
• Involving everyone on performance to mitigate
risks :
✓ Conception and architecture
✓ Unit performance testing
✓ Load and datasets definition
✓ Capacity planning
✓ Monitoring application (user experience), server
(resources) and usage (load injected)

8. Automationlimits
• Automate as much as we can but still need for manual action
• Performance testing
✓ Correlation rules
✓ Client side variabilisation
✓ Think time
✓ Pacing
✓ Datasets
✓ Project maintenance
• Monitoring
✓ Dashboards
✓ Alerting
✓ Configuration
• CI/CD evolutions and maintenance

9. Continuous Performance implementation

10. Continuous Performancein practice
Category Goals Example
Performance testing • Load and stress testing
• Improve future tests with framework parameters
Neoload
Monitoring • Analyze your load tests performance issues
• Compare your production cases with your tests
Dynatrace
Continuous
performance
• Capitalize on your load tests to avoid performance
regression
• Test and monitor automatically after each release
Jenkins
Deployments • Automate the application deployment for consistency
• Scale easily by creating injectors instances on the fly
Openshift
Generating data • Capitalize on data you need for your load tests
• Automate data generation and the environment test prior to
your tests
Mockaroo

11. Continuous Performancein practice
• And more:
✓GitLab for source repository
✓JUnit for unit test in Java
✓Maven for dependencies build
✓SonarQube for code quality analysis
✓Nexus for binary repository
✓Ansible for deployment and configuration scripts
✓Selenium for browser simulation
✓Slack for smooth alert notification
✓Elastic Stack for log analysis

12. Continuous PerformanceArchitecture
Test Environment
DeployCommit
Notification Run build
Get source code
Publish test
reports
Run tests
Stress the
application
1
2
3
4
6
7
78
5
Retrieving
metrics
7 Neoload & Dynatrace integration

13. Demo

14. Q & A
Thank you !