Strategy First: How AI Enters Regulated Medical Labs
AI in a regulated medical lab sounds like a compliance nightmare. Here is how a strategy-first approach made it work without breaking the rules.
Podcast: Software Testing, AI and Quality Insights : Page 2 of 16
When generative AI violates your own values
Generative AI cannot be used ethically as long as training data is used without consent and billionaires control the models.
Why COBOL Developers Prefer Writing Tests in Java
COBOL developers found writing unit tests in Java easier than in their own language. The interface architecture made it work on a mainframe.
Patient agility: Is agile working dying?
Agility is ailing in many companies, but it is not dead. Two rescue service schemes show where the pain is and what can help now.
Why Traditional Testing Fails for AI Systems
Testing a chatbot breaks every rule traditional testing relies on: same input, wildly different outputs, and bugs that live outside the code.
Developing security requirements as a team
Security requirements stay abstract until a team defines what the system actually protects. CIA goals and threat modeling make it tangible.
Why Testers Are Safe Despite AI Hype
AI won't replace testers, but it will shift what they do. Where it saves real effort, where it still needs human control, and why starting now matters.
Why agentic engineering changes everything
If you have bad principles and use AI, you will get worse faster. Why agentic engineering needs more than code generation.
How to Build QA Culture in Your Company
Building a QA culture takes more than good tools, it starts with listening carefully. Stakeholder views of quality become a shared roadmap.
The Blue Angel for software
The Blue Angel for software exists and gives measurable values for energy use and hardware life. What the certification costs and returns.
Why Quality Engineers Fail at Business Thinking
Testers who can't explain what their work costs, or saves, are easy to cut. Here's how business thinking changes that.
Security tests for AI systems
AI systems can be tricked into revealing protected data through clever prompts. Where the points of attack lie and what OWASP recommends.
Building Trust with AI Agents
Trusting an AI agent works the same way as trusting a colleague: you need clear communication, checks, and a system that catches what the model gets wrong.
Why AI cannot do cause-and-effect - and QFD helps
Why can't LLMs do cause-and-effect? And what does Quality Function Deployment have to do with it? The answer changes how you prioritize testing.
Why Your CI Pipeline Is Lying to You
Flaky tests don't just slow down CI, they erode trust until engineers stop believing the build. Here's how to fix that.
Practical and safe development with IEC 62443-4-1
Standards do not make secure software by themselves - but 62443-4-1 shows how security really belongs in the process from the very first design step.
From Nokia to iPhone: What Pen Testers Learned
From analog eavesdropping to overlay attacks on banking apps, mobile security has never stopped failing users in new ways.
From developer to test automation engineer
From developer to test automation engineer: Why clean code principles decide which automation lasts for years and which doesn't.