DiscoverSoftware Testing Unleashed - QA, DevEx & Quality Engineering
Software Testing Unleashed - QA, DevEx & Quality Engineering
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Software Testing Unleashed - QA, DevEx & Quality Engineering

Author: Richard Seidl | Software Development & Testing Expert

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Software testing is no longer just a phase—it’s the foundation of modern engineering and your ultimate competitive advantage.

Welcome to Software Testing Unleashed, the weekly podcast for anyone dedicated to building better software, faster. Hosted by Richard Seidl, renowned expert in software development and testing, this show is your backstage pass to the tools, tactics, and trends defining the next era of Quality Engineering.

Whether you are a QA Engineer, SDET, Developer, or Tech Leader, each week we bring you field-tested insights from the brightest minds in the software universe to answer the industry’s toughest questions:

- Smart Automation: When should you automate, and when is it a trap?
- AI & ML in Testing: How do you maintain quality in a world of non-deterministic code?
- The "How Much" Dilemma: How much testing is actually enough for your specific scale?
- Architecture & DevEx: What makes a great integration test and how do you improve developer experience?

From scaling QA strategies in enterprise projects to building your very first test suite, we bridge the gap between complex theory and practical execution. We dive deep into CI/CD, Cloud-native complexity, and the future of manual vs. automated testing.

🚀 Ready to unleash the next level of quality? Hit play, subscribe, and join a global movement of software professionals shipping with confidence.
75 Episodes
Reverse
Testing is supposed to prevent failures in production, yet sometimes the test itself is what breaks things. Bart Knaack calls this the dark side of testing, and we talk about test data leaking out of poorly protected environments, test systems that give attackers a look at your code, and why testing in production needs a risk assessment of its own. Bart would rather ask questions than hand out checklists, because a ticked box stops people thinking. He also tells the story of a team with 10,000 end-to-end test cases that ended up needing only 50.
Quality hunting grew out of Rik Marselis's frustration with bug hunting, and I wanted to know how it works when the goal is information about quality. Rik walks me through a session: teams of about four people, three hours of exploratory testing and a goal agreed up front by asking stakeholders what would help them sleep well at night. He explains why the report structure comes first, why the prize should stay small and why the point just before several sprints go to production suits a hunt well.
What does it take to turn a risky one-time event into a conference that testers travel across borders for? With Okan Çakmak I talk about how the Istanbul Software Testing Conference grew out of something missing: after the pandemic, the big Turkish testing events stayed online, and people lost the chance to meet, network and hug each other in person. He tells me how the team wondered whether 50 or 100 people would come, then welcomed 200, and how the "light in their eyes" of those first participants convinced them to keep going.
What happens when AI ships poor quality faster, and nobody notices because the pipeline stays green? With Michaël Pilaeten I talk about why test automation without a clear strategy is a problem that AI only accelerates, not solves. We get into the question of what separates a tester from a test automation engineer, why those two roles need each other but are not interchangeable, and how chasing 100% coverage on millions of lines of legacy code can be a waste everyone agrees to quietly.
What separates a handful of agents from a real agentic system? With Nishan Portoyan I talk about exactly that line, and why most teams are still on the wrong side of it. We walk through a full testing pipeline where eight to twelve agents handle everything from requirement review to performance test conversion, and I keep coming back to the question of where the human has to step in and why the AI simply cannot replace that judgment. Nishan is direct about what these systems cannot do: they do not think, they gather information and hand it back, and anyone expecting otherwise will run into trouble fast.
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