QA/QE

What if your testers could build? What if, instead of slowing down releases, your QA team could help you ship faster—while raising your product quality?

This talk shares how testers at QE 360 didn’t just learn automation. They became full stack engineers with a deep foundation in quality—taking on frontend, backend, cloud, and even AI-driven test engineering.

You’ll hear real stories of transformation:
Career-shifters who now outperform CS graduates.
Testers who used to wait for devs, now driving builds and CI/CD.
Teams that used to be the bottleneck, now becoming the accelerator.

This isn’t just a talk for QAs. It’s a call to action for tech leaders to unlock the full potential of their testing teams—without needing to expand headcount. If you’re a manager tired of manual testing delays, or a director looking to modernize your SDLC without relying solely on vendors, this talk gives you the blueprint. You’ll walk away inspired—and ready to turn your testers into builders.

This talk covers how to adapt software testing and quality management to the use of AI technologies. It includes understanding what are the opportunities where AI can be used in quality assurance and understanding the practical tools available. It also aims to provide the audience options on how they can start to use AI in testing.

Automated tests are crucial for efficient software development, but flaky tests that inconsistently pass or fail without code changes can severely undermine their value. Such non-determinism sends confusing signals to the team, making it difficult to interpret test results. This erosion of trust in the test suite leads to disregarding signals, integrating features with real failures, and nullifying the purpose of testing. Instead of speeding up progress, flaky tests slow it down, obscure actual bugs, and increase costs. As a result, automation in testing becomes a double-edged sword if not properly maintained.

In this talk, we will examine the causes and consequences of flaky tests, particularly within Continuous Integration/Continuous Deployment (CI/CD) pipelines. We explore how flaky tests erode trust in test results, delay releases, consume resources, and mask genuine bugs.

We delve into the common pitfalls of test automation that lead to increased maintenance overhead and decreased reliability. We also emphasize the importance of establishing testing guidelines, leveraging reporting tools, using artificial intelligence (AI), and documenting flaky tests to improve overall test suite health.

I will also share practical and effective mitigation strategies, emphasizing the importance of code quality, design patterns, advanced tools, comprehensive logging, team collaboration, and regular reviews.

Finally, we will discuss actionable steps for creating meaningful automated tests, focusing on principles derived from real-world situations from my experience. By adopting these strategies, engineers can build a more reliable and trustworthy test automation suite, leading to faster and more stable software releases.

Key Takeaways:
1. Evaluate how flaky tests can erode trust and slow down developments.
2. Analyse the context and purpose of tests to avoid common pitfalls.
3. Concrete strategies for identifying and mitigating flaky tests.
4. Exploring the role of AI and reporting in creating reliable tests.