Building Quality Gates and Automated Decisions in CI/CD Pipelines. Lessons Learned with 200,000 Regressions.

location_city Chicago schedule Mar 29th 02:50 - 03:30 PM CST place Sauganash West

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. Teams must make fast and reliable decisions by tracking test results of thousands tests for hundreds of runs triggered by code changes. 

But what if we could leverage Machine Learning to do the analysis and help team make rule-based decisions using a concept named Quality Gates?

In this presentation, I will share learnings derived from regressions in 200,000 test cases, and how we use Machine Learning (ML) to triage failed reports and reduce team effort on results analysis. How we organized a Quality Gates and formed a rule-based engine to identify Go and No-Go decisions for a particular set. And how we automate release pipelines by use of meaningful feedback from the automated testing results.

You will learn how to build Quality Gates in ReportPortal, how to integrate feedback within CI/CD pipelines. How to utilize ML analysis in triaging of failed reports. And finally make a leap forward to Continuous Testing.

 
 

Outline/Structure of the Presentation

  1. Learnings and pain points derived from 200,000 regression sets
  2. Minimization of effort to triage failed reports
  3. Extension of automated test cases with attribute, related to components, priority, severity, flows
  4. Forming the dashboards for component health checks and environment checks
  5. Building Quality Gates based on rules and test case attributes.
  6. Organizing sequences of pipelines in Ci/CD tool based on automated QG decision
  7. Outcome and time savings

Learning Outcome

You will learn how to :

  1. Build Quality Gates in ReportPortal
  2. Integrate test automation feedback within CI/CD pipelines
  3. Extend automated test cases with attributes for detailed structuring
  4. Utilize ML analysis for triaging of failed reports

Target Audience

QA Managers, QA Leads, Testing team leads, mid - senior test automation engineers

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