Let’s inspire, share, and grow
in all aspects of software
quality and testing

Testers often struggle to get the recognition, influence, and impact they deserve. But what if the problem isn’t what we’re doing, but how we see it? Our stakeholders are like customers with problems to solve. When we start viewing our work through that lens, everything changes. In this talk, we’ll explore how a little entrepreneurial thinking helps testers ask better questions, focus on what truly matters, and deliver value in ways that actually get noticed. Testing isn’t just about box ticking. It’s about solving the right problems for the right people.

This experience report is about our DevOps journey, and how Testers are driving progress shipping on a B2C platform with hundreds of services faster and more frequently. We had automated deployments. Infrastructure is in code. UI tests written by “Full-Stack” QEs are defined in jobs, and had unit and UI tests in PRs and builds. We were already tracking DORA metrics by service. But we were still releasing with bespoke validation/signoff processes. We could ship in minutes/hours, but it took live coordination, approvals, and people pushing buttons to enable deployment. Then we brought it all together. Our Platform Engineering Team, QEs, and product engineering teams collaborated to iterate on tooling that lets our engineers merge to Main and walk away. We’ve learned, and solved technical and social/process problems. I’ll share what we’ve done, what we think we’ve learned, and ask for your feedback.

We have data. A lot of data. In many organizational areas - including yours. Performance metrics and dashboards are popping up everywhere. Are they helpful? Let's ask together why we have these dashboards. We'll uncover the main problem with useful visualizations and show how to use the concept of “data ops” to achieve helpful information consumption in organizations. We shed light on the human part of organizational information processing.

Data has become the new gold in today's digital age, and Data Science along with Machine Learning are at the forefront of innovation. However, despite the excitement surrounding these fields, the role of testers in data science remains relatively unexplored. Many testers lack exposure to the unique challenges of testing data science models, despite their growing importance in the tech landscape. In this talk, I will share my journey into the world of data science model testing—a field I had never tested before—and demonstrate how I was able to bring value to a project in unfamiliar territory. My goal is to inspire fellow testers to venture into this space with confidence, equipping them with insights, strategies, and processes that can help ensure the quality of data science models.

Lizard Optimization is a way to gain deep understanding of underserved customers by mixing exploratory testing, observability and product experimentation. This talk is a quick overview of the Lizard Optimization process, with a case study of a product where it helped unlock 500 times (not percent) growth year-on-year. Learn how to steer your product growth gently without overcomplicating delivery and focusing on value, and how cheap and dirty observability can provide the necessary information to make informed decisions, understand your audience better and improve your products.

While testability may appear to be an obvious consideration in software development, my 20+ years of experience tell a different story. Testability is often misunderstood, sidelined, or underestimated. This leads to frustration and hinders productivity. Developers and testers approach testability differently, shaped by their roles, priorities, and understanding of testing. These differences impact how testability is defined and addressed. Testing goes beyond verification—it’s about challenging, questioning, and exploring software to uncover risks. Testability should reflect this broader spectrum, but this perspective is often overlooked. In this talk, I will share my experiences on how addressing testability from a holistic approach—considering different aspects and perspectives—can boost productivity and enhance test coverage. These improvements benefit developers, testers, and business organizations alike.