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

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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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