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Neotys PAC 2018 - Bruno Da Silva

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.

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Neotys PAC 2018 - Bruno Da Silva

  1. 1. Continuous Performance Innovative Approach Bruno Da Silva
  2. 2. Performance 101
  3. 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. 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. 5. Continuous Performance challenges
  6. 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. 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. 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. 9. Continuous Performance implementation
  10. 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. 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. 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. 13. Demo
  14. 14. Q & A Thank you !

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