Presenting Speakers & Sessions

Back to Basics! 

I´m worried. Many testers can´t explain how they actually work, how they decide what to test, why something matters, or when it´s safe to stop. Some say they use "common sense", but when I ask what that means, how it works, and how someone can learn it.... the room goes quiet. 

So how do testers determine what needs testing? And how do they know when they´ve tested enough? 

These are the hardest questions in our craft. Organizations often fear that testing isn´t focused on the risks that truly matter. Thinking about risk and test strategy gets skipped because "the sprint is too short".  Recently, I spoke with a university lecturer doing PhD research on how to teach testing in higher education. He interviews testers about how they think and how they make decisions. His early results match what I see in the industry: most teachers can´t explain their own reasoning. That is a serious problem. If you can´t explain your decisions, you can´t judge when you´ve tested enough. And because testers are naturally inclined to test too much, we often waste effort and still miss what matters. 

The solution is simple: go back to the fundamentals.  Automation and AI are powerful, but they don´t help if your testing basics are weak.  Automation is only effective when you automate the right things. AI still gives wrong answers, so how will you spot them if your foundation is shaky? We need to return to the core skills of testing. Testers must learn how to think about testing. This talk is about how we can test better, faster, and more cost-effectively, while achieving excellent results. We´ll identify the fundamental skills, the mindset behind great testing, and practical ways to learn and practice them. 

Key takeaways:

• A clear view of the essential skills every tester needs 
• How to think and talk about testing 
• How to decide what to test
• How to determine when you´re don 
• Practical tips and techniques to develop these skills 

30 years in testing, battles won and battles lost 

May 1st 1995 I started my career in testing, moving from a job in the air force to a role of testing military software - before the agile manifesto, most team automation and test training. Looking back to where it all begun I realize that I was so privileged to work as a tester in an agile team, before agile was defined. Having the role as a tester with the deep domain knowledge that helped me understand the needs of the users and help my team do the same. Much have changed over the years, I have created a good backpack of skills - based on the training and practical experience I have gained over the years - a backpack full of stuff that can help me help others. And now the world is so different from then; different platforms, different roles, agile frameworks, AI and more - so a whole other world for med as a tester... or is it?

The essence of what I do and what I stand for hasn´t changed much. I work with the focus for building bridge between the end user and the project, doing my best to understand the needs of both. And I use my testing skills together with my ability to build relations to others to do this. 

In my presentation I will take you through examples from those 30 years of testing, illustrating that the center of all technology is people, and being able to communicate and assist in ensuring that our end users get the value they search for is key. 

Key takeaways:

• Human centered testing focus - make a difference to your end users
• Test techniques still add value - use them together with your end users
• Communication as a critical skill: especially the ability to relate well with both users and project teams

Nobody Is Coming To Save You! 

Imagine you're on Dragon's Den. You've got two minutes to convince a panel of investors that your role is worth funding. You step up and say: "What I do is click around and try to break things. I write loads of test cases and track how many pass or fail. I can't tell you exactly what I'll find, how long it'll take, or what value I'll bring. But I promise, me and my work are valuable and important."

Would you invest? - Me neither 😐

OK I took some artistic license there, but it's uncomfortably close to how some in our profession describe themselves, their work, and their value. We talk in features when we should talk in benefits. We describe activities when we should describe outcomes. We say "trust me" when we should show evidence. For a long time, we could get away with it. Over the past few years, that has become harder. Now? It has to stop. ASAP.

AI coding tools are generating a tsunami of code. Agents are producing pull requests faster than teams can review them. The volume of output is exploding, and with it, the volume of ways things can go wrong. Hallucinations, drift, compound errors, non-deterministic behaviour. The systems we're shipping are fundamentally different from what we've dealt with before.

But plot twist!

This actually makes testing more important, not less. The "outer loop" of production verification, risk assessment, grey failure detection, and systems-level thinking is wide open. Nobody is closing it. And the people best equipped to close it are those who've spent their careers understanding how software fails in the real world.

However, if testers can't describe that value clearly enough for a CTO to understand, fund, and defend to their board, it doesn't matter how important the work is. It won't survive the next reorg. The AI era is forcing a reckoning that was always coming. Describe your value in terms your stakeholders care about, or watch someone else (or something else) take your seat.

In this keynote, I'll combine lessons from years of helping testers communicate their worth with a practical look at where testers can have the most impact in an AI-driven world. We'll explore why testers describe their work the way they do, what the Code Tsunami means for quality and risk, and how to reframe what you do using concepts from sales, Jobs To Be Done, and the awareness stages your stakeholders are actually at. You'll leave with a sharper understanding of your own value and the language to make other people see it too.

 Key takeaways: 

•  Describe your value like a knowledge worker, not a manual labourer. Understand the difference between features (test cases executed, automation coverage) and benefits (risks mitigated, incidents prevented, shipping confidence). Learn to speak in outcomes that matter to the people who control your budget. 

• The Code Tsunami makes testing more important, but only if you can articulate why. AI is generating more code, faster, with new categories of failure. The outer loop (production verification, risk assessment, behavioural drift detection) is where testers can have the most impact. Understand what that work looks like and how to position it.
• Use frameworks from sales and product thinking to communicate your value. Jobs To Be Done, the 5 Stages of Awareness, and benefit-led communication aren't just for marketers. They're survival tools for any tester who wants to keep their seat at the table. You'll leave with practical ways to apply them immediately.
  
   

Physical things in a digital world - an over 3 decades journey in data quality, human perception and AI

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Testing AI systems in practice - A case study

AI is included in more and more systems, and we need to adjust our testing methods and practices. We often talk about how AI can help testers, but how do we assure the quality of the AI functionality itself?

This was my main focus when I started my assignment as a test manager on a project to develop the first version of an AI system. I had been involved in testing of a GenAI plattform before and also read a lot about how to test AI systems, but it is a new area and there is still a lot to learn. This was also a larger system with complex functionality, and I felt that we needed to find methods that worked for us in this first iteration of the product but also could support the coming road map.

The system´s purpose is to provide the user with insights from a large amount of data in different formats and from different data sources. It includes several AI technologies, for instance speech-to-text, AI search, and a chat function using Gen AI. It also includes a lot of other functionalities, for instance statistics, document management and map functionality. All of this needed to function properly to give the user the value they needed as well as the confidence in the responses. 

In this presentation I will talk about strategies and methods used in the project and about the lessons we learned. Testing of AI systems is new when compared to testing to testing of traditional systems and we need to share our experiences to build upon best practices. 

Key takeaways: 

• Examples of useful strategies and methods when testing AI systems.  

• Examples of pitfalls when testing AI systems and how to avoid them.  

• Examples of test automation techniques for testing AI systems.  

So much more than test cases – unit and component testing in long-lived systems

Long-lived products like the Gripen fighter aircraft demand software that remains reliable, efficient and maintainable for decades. As software systems grow in size and complexity, the cost of testing grows with them – yet simply adding more test cases is neither sustainable nor sufficient. Drawing on experiences from six large-scale industrial companies, this talk presents a structured approach to unit and component testing that combines complementary methods and tools into a coherent strategy, which can help companies cut costs, save time and identify defects earlier in the development cycle. The talk closes with a broader reflection on what sustainable testing practices will mean for the software industry in the future. 

Feedback is Data, not Drama 

Who hasn´t felt dread when hearing "I have feedback for you"?  This keynote shows testing teams how to treat feedback as data -not drama - for honest conversations about quality without defensiveness. 

High-performing teams use feedback loops (code reviews, test results) to catch flaws early, starting with psychological safety. Grounded in Google´s Project Aristotle, the institute for Corporate Productivity, and the Center for Effective Organizations research, you´ll gain tools to boost results, engagement, and collaboration. 

Key takeaways: 

• "Feedback-as-data" mindset for smarter decisions

• Listening techniques separating facts from stories

• Concrete skills strengthening tester-developer teamwork 

Using Brain Science Principles in your day-to-day work

This session explores how insights from brain science can make day-to-day work more engaging and effective. Our brains are constantly scanning for meaning and emotional relevance, and when these elements are missing, attention drops quickly. Research shows that the brain begins to disconnect after about ten minutes when nothing changes - an important reminder that even small shifts can ignite focus.

Curious about how the brain shapes our behavior, I have discovered how simple adjustments make it easier to stay present and engaged.

In this session, you’ll learn practical neuroscience principles and how to apply them to activate your brain, keep attention, and bring more life, energy, and creativity into your daily work. The goal is to help you work in a way that feels more dynamic, human, and mentally aligned with how the brain functions.

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Testing our Testware – Getting the Whole Truth

When planning our testing, we often put significant effort into defining metrics to ensure that requirements are covered and that each is validated by at least one test case. When these metrics are set up correctly, they give us a clear view of progress, the risks addressed, and the remaining residual risk, allowing us to prioritize further testing activities. However, metrics that appear solid on the surface are not always reliable. Beneath the numbers, inconsistencies in testware can undermine the accuracy of reporting and lead to incorrect conclusions.

Examples include:

• User stories being modified without corresponding updates to acceptance criteria

• Test cases being executed without proper traceability

• Defects being closed without retesting

• New functionality being introduced after test analysis has been completed

These issues can create a misleading basis for reporting and ultimately result in poor decision-making by stakeholders. In a recent project, we discovered that despite having comprehensive and well-defined metrics, inconsistencies in our testware significantly impacted the quality of our reports. To address this, we introduced a governance model supported by a wide range of advanced metrics.

While effective, this approach proved time-consuming and required substantial ongoing maintenance. In this presentation, we will share and discuss this governance approach, the lessons learned, and how we plan to improve future implementations by introducing AI-assisted support to increase efficiency and sustainability.

Automating Acceptance Testing with AI Agents: A Practical Case Study from Heidenreich

Frequent releases and complex systems makes consistent regression / user acceptance testing difficult, both for developers and product owners. In collaboration with Heidenreich, we explored how AI-powered browser agents can be used to automate acceptance testing with minimal setup effort. This session presents a hands-on case study showing how prompt-driven testing can cover real user journeys, reduce manual effort, and uncover issues traditional testing often misses. We'll look at how user stories can be transformed into dynamic test scenarios executed by AI agents, and discuss both the opportunities and current limitations of this approach.

Key takeaways: 

• How AI “browser agents” can execute acceptance and regression tests using natural language prompts.
• How to write testing prompts that turn user stories into automated tests.
• Practical benefits: reduced testing time and improved coverage.
• Flexibility around tools and plattforms.
• Limitations: where AI testing complements (not replaces) traditional automated tests.
• How this testing can create feedback loops for improving product quality

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