Advertise on podcast: Software Testing Unleashed - QA, DevEx & Quality Engineering
Rating
5from
This podcast has
76 episodes
Language
EnglishExplicit
No
Date created
2025/05/27
Latest episode
2026/10/22
Average duration
24 min.
Release period
7 days
Description
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.
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Check latest episodes from Software Testing Unleashed - QA, DevEx & Quality Engineering podcast
Testing AI Models as Magic Boxes: Why Checks Fail - Michael Bolton
2026/10/22
Testing AI models means testing a magic box: nobody has the source code, and the behavior is neither controlled nor understood. Michael Bolton calls that strongly magical, and with him I talk about what follows from it. His principles are simple: if something matters very much, don't depend on magic, and if you use it anyway, responsibility demands that you test it and maximize testability. Simple output checks don't stand a chance against a language model, so testing becomes experiencing, exploring and experimenting, like peeling an onion. And Penn and Teller's clear plastic cups show why we fall for these models in the first place: the insight we see in their text comes from us.
Dark Side of Testing: The Failures We Cause Ourselves - Bart Knaack
2026/10/15
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: Testing for Information Instead of Bugs - Rik Marselis
2026/10/08
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.
On-Site Testing Conferences: Why Networking Needs Presence - Okan Çakmak
2026/10/01
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.
You Need a Test Automation Strategy Before AI - Michaël Pilaeten
2026/09/24
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.
Agentic Systems in Testing: Why Humans Stay in the Loop - Nishan Portoyan
2026/09/17
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.
AI in Software Testing Raises the Value of Expert Testers - Olivier Denoo
2026/09/10
What happens to quality when anyone can generate code with a prompt but nobody checks whether it actually works? With Olivier Denoo I talk about why that question puts expert testers in a stronger position than many managers currently assume. We get into the real risk of AI that always sounds right, the edge cases a lost suitcase in Brussels reveals, and why the hardest unsolved problem in testing is still the same one Olivier ran into three decades ago: describing precisely what you want. The skills I keep coming back to in this conversation are business understanding, communication, and the ability to spot what the AI confidently got wrong.
Lean Management: Why Problem Analysis Beats Quick Fixes - Nirmala Saneechur
2026/09/03
Why do we rush to fix problems before we actually understand them? With Nirmala Saneechur I talk about Lean management, what it means to deliver quality at the right time rather than just on time, and why spending a full day understanding a problem is not wasted time. We get into what waste really means in a software project, including the story of 15 features a client never used, and how working in silos creates the kind of back-and-forth that slows everything down.
Performance Monitoring: Set a 70% CPU Alert Before Failure - Rao Dhaligadoo
2026/08/27
Slow systems don't just annoy users, they cost companies real money, and most teams only find out when the phone starts ringing. With Rao Dhaligadoo I talk about what it actually takes to catch performance problems before customers do. We get into how a 70 percent CPU threshold can trigger an automated chain of monitoring, ticket creation, and root cause analysis, and why that matters more than it sounds. Rao also shares what it feels like to spend 45 hours in a war room at a bank, watching a CIO walk in at 1 a.m. while the whole team sits in silence, and how that experience shaped the way he thinks about proactive testing.
End-to-End Test Automation Without the Maintenance Trap - Lilia Gargouri
2026/08/20
Forty percent of automation time lost to flakiness and maintenance: that is the reality Lilia Gargouri describes. I talked with Lilia about end-to-end test automation at scale, and what struck me most was how far back the real solutions reach: a single HTML attribute added in 2017 still pays dividends today, simply because the team decided to use it consistently and never change it. We get into the full picture, from how to accurately identify every UI element without XPath gymnastics, to why fifteen well-chosen test cases can outperform five hundred noisy ones, to naming conventions and layered library architecture that keep maintenance from spiraling. And for anyone sitting on an old, flaky test suite right now, Lilia has a concrete answer for where to start.
API Testing: Agent-Optimized Design Cuts Token Costs - Sebastian Małyska
2026/08/13
With Sebastian Malyska I talk about what sits behind the APIs testers often take for granted. We get into why REST is still on top but not the whole story, how GraphQL and webhooks fit into the picture, and what actually happens when an AI agent talks to a real system through MCP. There is a practical argument running through all of it: if you understand the protocol layer, you can make smarter decisions about where to test, how to structure automation, and how to avoid burning unnecessary tokens and budget. Sebastian also makes the case that front-end testers are already closer to this world than they think, and that the next step in the career is simply to look one layer down.
LLMs in Software Testing: Start with Small, Bounded Tasks - Klaudia Dussa-Zieger
2026/08/06
With Klaudia Dussa Zieger I talk about where language models actually deliver in testing and where they fall flat. We get into the concrete details: why a small function like generating keyword documentation works so well with a standard chat model, why reviews only started working once they switched to a reasoning model, and why one engineer working on embedded legacy code said no to AI help without hesitation. Klaudia also shares the translation use case I found most vivid, turning a cryptic technical defect message into something a domain tester can actually read.
Testing Complex Systems: Ship Small and Learn Fast - Jean Francois Riverin
2026/07/30
With Jean-Francois Riverin I get into a question that bothered him for over 20 years: why are there still bugs in production even when you tested as well as you possibly could? His answer is that software stopped being a complicated problem, like building a bridge, a long time ago. It is now complex, more like medicine or meteorology, and that means the whole idea of finding the right answer through testing is the wrong goal. We talk about what it looks like to switch that mindset, what "safe to fail" means in practice, and how to make that argument to a manager who still expects predictable budgets and zero surprises on Friday afternoons.
Holistic Engineering: Non-Technical Forces Shape Your Code - Vanessa Formicola
2026/07/23
Why do so many software teams end up with the exact architecture they never intended to build? With Vanessa Formicola I talk about the hidden forces that shape technical decisions, and why those forces have far less to do with technology than most engineers want to admit. We get into patterns she calls the kitchen sink, Cirque de Soleil coding, and the monolith constellation, each one a recognizable trap that grows from career incentives, delivery pressure, and organizational culture rather than bad technical judgment. I keep coming back to her concept of holistic engineering, which asks us to treat an organization as a living system and factor in product direction, people's actual lives, and structural dynamics when making architectural choices.
Children's Book About Testing That Inspired a Future Tester - Kari Kakkonen
2026/07/16
With Kari Kakkonen I talk about what happens when you explain software testing through a fantasy story for children, where dragons are defects and knights are testers. We get into how the analogy grew into a full world covering functional, performance, usability, and security defects, each mapped to a chapter with its own kind of dragon. Kari describes how he tested the book iteratively with children, teachers, testing professionals, and non-IT readers across four rounds, and how a twelve-year-old at a book fair said she wanted testing as a profession after flipping through it. We also touch on the donation campaign that brought the book into schools across Finland and beyond, and what a possible second volume might cover, from testing techniques to automation and AI. One detail I keep coming back to: not every analogy in the book is spelled out, and the black dragon is left without explanation on purpose.
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