Software testing is constantly changing. Artificial intelligence dominates the discussion, influencing test data, automation and role models. Uncertainty in the job market is growing as traditional tester and QA lead positions become rarer and companies rely on efficient tools and low-code solutions. At the same time, complex systems continue to require specialist knowledge: Only those who constantly educate themselves, understand technical basics and think outside the box will remain relevant.
In this episode, I talk to Daniel Knott about the now in software testing: AI, agents and our toolbox. We reconnaissance productive use cases such as document analysis, test data generation and copilots, talk about MCP and agents and ask ourselves where real added value is created. At the same time, we look at the risks: more generated code, questionable quality, upcoming refactorings. What does this mean for us testers? A deep system view, domain knowledge, clear communication within the team, re-sharpening the pyramid and quadrants.
"I've often seen that AI gives you a completely different view of data or data correlations, so that you can make the right decisions." - Daniel Knott
Daniel Knott loves digital products with high quality, be it web or native mobile applications. He has worked in the IT industry for almost 20 years and has experience with hands-on software testing for desktop, web and mobile applications. He has also worked as a product manager for mobile and web products. Currently, Daniel works as an IT manager as Head of Engineering, helping software development teams deliver great products with high quality.Daniel has written two books - 'Hands-On Mobile App Testing' and 'Smartwatch App Testing' - and is a frequent blogger and conference speaker. In 2022, he also founded his YouTube channel about software testing, which now has more than 145,000 subscribers.
The world of software testing is changing rapidly. New technologies, changing job profiles and the influence of artificial intelligence present the community with exciting, and sometimes scary, questions. In the latest podcast, Richie joins testing legend Daniel Knott to shed light on what testers are facing today and what opportunities are available to ensure they don't fall behind.
Hardly any other topic occupies the testing community as much as AI. Daniel reports that not only major trade magazines but also local advertising newspapers are now dealing with it. For many, it almost seems like a constant buzz - yet AI is clearly driving testing forward.
One example: tasks such as generating test data or evaluating large amounts of data have become much easier with AI solutions. Copilots can explain code, find correlations or help write scripts for automation, even if you are not a professional developer. At the same time, uncertainty is growing: will so much code be generated by AI in the future that quality will suffer? Who will ultimately check the work of the machines? Testers will remain in demand - with the difference that they will have to adapt quickly to new things.
With new tools and AI support, there is a growing risk that old job profiles will disappear. Many companies are streamlining their QA departments. Experienced specialists, such as test managers or QA leads, are currently particularly affected.
Daniel clearly states: "Many experienced testers and developers are looking for new jobs on LinkedIn in particular. In the regulated sector, things are looking a little more stable. But here, too, many testers are feeling the pressure. Management often makes decisions based purely on numbers and cuts testing first and foremost - according to the motto: "You can cut something, the developers will do it."
In addition to AI, test automation is also undergoing change. Many companies are increasingly relying on low-code and no-code solutions so that even non-experts can write tests. At the same time, however, teams are realizing that the more levels and dependencies each new application has, the more it requires different test strategies.
The good old test pyramid is coming back into the conversation. Companies are checking: Which tests do we really need at UI level? Where can we save time and effort by testing deeper? Communication between developers, testers and product managers is becoming more important than ever. This is where simple models such as the test pyramid or agile testing quadrants show their value - they help to start discussions and define clear responsibilities.
What should testers be able to do today? Daniel advises: Don't just focus on technical skills or automation. Anyone starting out should consolidate the basics: What are test types, how do you describe errors, what are key terms? With some experience, it's worth understanding system architectures - in other words, how modern applications and entire system landscapes work.
Important: Industry-specific knowledge counts more and more. AI can evaluate data today, but it cannot replace expert knowledge. In addition, non-functional requirements such as security and accessibility often remain marginal topics - but there is a lot of potential here. So stay tuned and pick a field that interests you.
Highlight of the episode: The open, supportive climate in the testing community. Knowledge is shared at conferences, in blogs, on YouTube - like on Daniel's own channel - and questions are discussed openly. This pays off, especially in uncertain times, because learning from each other enriches everyone.
The world of testing is changing faster than ever before. AI, new tools and changes in the job market make the future exciting and sometimes stressful. Those who regularly undergo further training, keep an open mind and exchange ideas in the community will still discover many opportunities. Testing remains important - just not like yesterday.