How critical is automation in scaling quality engineering, and what are the key areas where automation should be prioritized to handle large-scale operations?
Priyanka, given your background experience, how might scaling quality engineering programs in a healthcare environment differ from scaling in consumer electronics, for example?
When scaling quality engineering across multiple regions or time zones, how do you ensure that distributed teams remain aligned and maintain consistent quality standards?
How does the CI/CD pipeline evolve as you scale quality engineering, and what are the key considerations for maintaining efficiency and reliability in large-scale deployments?
How important is real-time monitoring and feedback in a scaled quality engineering environment, and what tools or strategies are most effective for this purpose?
Can quality engineering programs and the QA/testing teams that support them grow on a 1:1 basis, or is there expected to be a different dynamic between team size, productivity, and outcomes?
How do you effectively govern the quality practices in large scale organization?
What best practices we need to follow to get scaling coverage ?
What career or training steps, if any, may be needed to become an automation transformation expert?
In your opinion, what are the most significant challenges that hyper-growth companies face in terms of maintaining accessibility standards in their software? How do you overcome these challenges?
What are the primary challenges faced in scaling quality engineering for a global user base?
What are some key lessons learned from Robust quality test that can help avoid common pitfalls?
Hi there!
As I attended this insightful session, I’d say the most valuable metrics include test coverage, defect density, and mean time to resolution (MTTR). These metrics give a clear view of the test effectiveness and the speed at which issues are resolved. Additionally, tracking release frequency and customer satisfaction scores can highlight the impact of quality engineering on overall product delivery.
Hope this helps!
Hey!
From my experience after attending the talk is that to start with a solid foundation in automation and CI/CD practices before scaling. It’s crucial to avoid technical debt early on. Also, keep the communication channels open between dev and QA teams to address issues before they escalate. This way, you can prevent bottlenecks as the team grows.
Hope this clears things up
Hello
I would like to answer on behalf of the speaker from what I grasped from the session is that tools like Selenium, Appium, and Cypress for automation, along with frameworks like TestNG or JUnit for structured testing, were highlighted. For CI/CD, Jenkins or GitHub Actions are vital. These, combined with monitoring tools like New Relic or Datadog, ensure that quality standards are upheld across large-scale operations.
Hope this is helpful!
Hey!
As testers, I would say testing in production requires a careful approach, such as using feature toggles to test specific functionalities without affecting all users. Synthetic monitoring and canary releases are also effective techniques to identify issues early on without disrupting the user experience.
Hope this helps clarify things!
Hi there!
Just sharing the thiught of the speaker based on her talk, maintaining a robust feedback loop involves using automated dashboards and regular sync meetings. Incorporating real-time alerts and continuous monitoring helps quickly identify issues and communicate them across teams. Collaboration tools like Slack or Jira are also effective for keeping everyone aligned.
Hope this helps!
Hey there!
As a performance tester, I say that it’s essential to conduct regular load and stress testing to understand system limits. Using cloud-based testing tools can help simulate high traffic scenarios. Additionally, having a scalable architecture and robust caching strategy in place ensures the system can handle sudden surges effectively.
Hope this helps!
Hi!
As I attended this insightful session, the speaker said that while it’s challenging to cover every scenario, a combination of automated and manual exploratory testing helps. Prioritizing critical paths for automation and involving the QA team in the early stages of development can significantly reduce regression risks.
Hope this helps clarify!
Hi there!
From what was shared in the session, using a blend of automated end-to-end tests, performance testing, and canary releases helps achieve high confidence in releases. Regularly updating test cases based on new features and changes also ensures coverage remains high.
Hope this helps!