Today’s modern development disciplines — whether Agile, Continuous Integration (CI), or Continuous Delivery (CD) — have completely transformed how teams develop and deliver applications faster. But in order to compete in today’s fast-paced digital economy, companies must also test faster, and test continuously. Tracking test results of thousands of tests for hundreds of runs, triggered by code changes. But what if we can leverage Machine Learning to find value in automated tests and reduce the time of result analysis?

In this presentation, he will share details about open open-sourced AI-powered test-automation dashboard — Use case of machine learning in test automation log processing and test fail categorization. With the benefits of real-time reporting from multiplied, distributed, or remote environments. You will learn how to start on your project and organize dashboards, and metrics tracking. Leverage the API of the tool for integration with your CI tools like Jenkins. Make a step to continue testing, bringing in Security and Performance testing results in one single entry point of testing results for your team.

Takeaways from the talk:

  •  The case study and easy-to-go solution augment the testing process with ML capabilities.
  • Practice to reduce team effort on failed results analysis.
  • Consolidation techniques to achieve visibility into the tested components and functionality.
  • Quality Gates establishment.

October 24 @ 09:00
09:00 — 09:45 (45′)

Dmitriy Gumeniuk