WeTest.Athens 2026

This presentation introduces the inner workings of the ISTQB and puts it into context.

1. How does a new syllabus come about?
2. Who decides what?
3. How does this world-leading software testing certification organization really work?
4. How does one go from idea to certification exam?
5. What is the ISTQB portfolio? Was there a complete paradigm shift? How does it relate to my career path?
6. What are the hot news and what can I expect from the ISTQB in a near future?

All these questions and many more will be discussed in a transparent, de-complexed and relaxed way with a twist of humor

Have you ever had that moment when mobile automation with Appium makes you question your life choices?

You can automate the web, you understand HTML, you know your way around selectors. So you open a native app and expect the same rules to apply. Then the first swipe fails. The selector that looked perfect stops working. The test gets slow. The logs look like noise. And suddenly Appium feels flaky.

But the truth is simple. Your tests are not failing because Appium is unreliable. They fail because YOU ARE doing it wrong. Native apps are not HTML, and treating them like they are will always backfire.

We will unpack what you are really interacting with when you automate a native app, how Appium bridges the gap through platform drivers, and why things like UI hierarchies, element attributes, and locator choices behave differently on Android and iOS. You will see the most common web assumptions people bring into native automation, why they create frustration, and how to replace them with a mental model that makes your tests faster, more stable, and easier to debug.

No live coding, hopefully just clear examples, sharp insights, and a new way of thinking about mobile automation.

It can be tempting to treat accessibility as just a checklist of technical fixes. But the truth is that accessibility requires a general awareness from day one - an expectation and way of thinking across your entire organization and the teams within it. Accessibility is a process, not a one-off project.
 
The talk splits into two parts:
 

• How to integrate accessibility into your organization
• How to design usable and accessible digital content

 
Designers, developers, researchers, testers, and product managers will leave with practical knowledge they can embed in their daily practices and workflows.

Modern software systems rely on large graphs of interdependent microservices maintained by multiple engineering teams. Nightly integration tests frequently expose failures in these systems, yet determining which team should investigate first remains a largely manual and error-prone process. Misrouted incidents lead to cross-team ping-pong, increased MTTR, operational friction, and wasted engineering time.


This paper presents MANTHOS (Microservice Alert Navigation & Triage Handoff Optmization Service), a probabilistic, cost-aware incident routing engine designed to automatically assign integration test failures to the most appropriate team. MANTHOS combines (i) a service dependency graph, (ii) diffusion-based prior estimation for likely upstream faults, and (iii) multiple operational evidence signals—including stack traces, recent code changes, service flakiness, and optional deployment or performance anomalies. It aggregates service-level probabilities into team-level responsibility and selects a recommended assignee using an expected time-to-recovery (TTR) optimization model that incorporates team responsiveness, current load, ownership alignment, and a synergy matrix capturing historical cross-team coordination patterns.


We evaluate MANTHOS on historical integration failures and demonstrate significant reductions in misassignment rates, unnecessary handoffs, and median TTR. The system integrates easily into CI pipelines, Slack/Jira workflows, and SRE tooling, providing transparent, explainable recommendations. Our results show that probabilistic inference combined with lightweight operational signals can materially improve dependability practices in complex service organizations. MANTHOS offers a structured, data-driven alternative to manual triage and serves as a practical foundation for next-generation AIOps tooling.

What if I told you that you can trick an LLM into revealing secrets, making bad choices or even acting against its own rules? AI may seem like a black box, but when you start testing it like any other system, surprising weaknesses start to appear.

Together with me, I want you to take a look into how the OWASP Top 10 (a well-known list of the most common security issues in software) can help to understand and mitigate the risks linked to LLM applications. We’ll go beyond theory and explore real-world examples both from the news and from my experience at ongoing projects and see how these vulnerabilities play out in AI-driven systems.

We’ll check how poorly crafted prompts, weak security settings, biased training data, lack of proper safeguards can lead to serious security flaws. How easily can an attacker manipulate an LLM? What happens when sensitive data leaks through unintended AI behavior? Can a seemingly harmless chatbot be turned into a security risk? I’ll answer these questions while using my own cartoons to illustrate key risks in a fun and easy-to-understand way.

The good news? You don’t need to reinvent the wheel to test AI. Strong system thinking, traditional testing techniques, critical mindset are already powerful tools for uncovering vulnerabilities. The same skills used to break and improve software (like exploratory testing, risk analysis, extensive logging and monitoring) can help make AI systems safer and more predictable.

By the end of this talk, you’ll have a fresh perspective on AI risks, practical strategies to make your LLM integrations more secure, and, of course, a few laughs along the way.

In 2019, the Lidl Plus app was born and cloud native hosted in webapps in Azure Cloud. In our process to maturity, we wanted to remove the vendor lock and we started  migrating to Kubernetes in 2022.
In this process, we modernized our performance testing environment as well, from master slave old friend JMeter to a K6 Kubernetes cluster using the K6 operator.
In this talk, we will be explaining how we are creating our K6 projects. How we organize and manage our projects creating a schema as a backstage project, so all the squad product teams can easily setup a new performance project.
Learn how we scaled our performance testing suite and our performance injecting system.
See how we created K6 extensions for the use case where there wasn’t an existing extension such as AzureServicebus.
Attendees will learn how to implement a project separated in a workload – user behaviour – scenarios, so the performance testing team can scale, and the code is easily maintained for the entire dev team.
You will also see how to use the performance testing in a CI/CD such as Azure DevOps or GitHub Actions, integrated with a monitoring tool such as Grafana.

Short one-liner: How Do We Trust Software Built by AI

Statement
AI agents are generating more code than ever, but how do you ensure its reliability? Learn why traditional testing fails and how Agentic Testing guarantees trust and scalability in AI-driven systems.

Abstract
AI agents are helping teams generate more code than ever before. Even the strongest believers in vibe coding caution against placing blind trust in that code. We are living through the fastest expansion of software creation in history, not because we hired more engineers, but because machines are now building software alongside us.This raises a fundamental challenge. How do you test and trust applications created by LLMs, assembled by multiple AI agents, and deployed by automated systems? The tools we rely on today were not designed for this reality or this speed. Unit tests and code reviews inspect logic and functions, but they were never meant to validate dynamic, evolving behavior. Traditional automation struggles when code is generated and modified continuously, becoming fragile and breaking faster than teams can maintain it. As AI agents become embedded into every layer of software, we are no longer just testing code. We are testing autonomous systems themselves.In this talk, we will explore why trust in AI-generated software can no longer be assumed, and why it must be continuously proven. Agentic Testing, the use of autonomous agents to test autonomous systems, emerges as the only scalable solution. These autonomous agents not only plan, execute, and analyze tests but also adapt and challenge systems the way real users would. Agentic Testing validates behavior, reasoning, and outcomes at the system level, ensuring that AI-driven systems meet real-world expectations.Drawing from real-world enterprise deployments and lessons learned building an AgenticTesting platform, this talk will reframe quality assurance as a strategic intelligence layer rather than a release-time checkbox. In the age of AI, trust isn’t just a feature, it’s a continuously earned asset. We’ll discuss how businesses can evolve their testing strategies to stay ahead in this rapidly shifting landscape.

Key Takeaways
● Why trust in AI-generated software must be continuously proven, not assumed.
● How Agentic Testing validates AI-driven systems by testing behavior, reasoning, and outcomes.
● Real-world insights on implementing Agentic Testing for scalable, autonomous validation.

In an era where digital banking is rapidly evolving, ensuring the highest quality standards in software development is not just an option, but a necessity. This talk will explore the crucial role of Quality Assurance (QA) in the neobank landscape, focusing on strategies to streamline QA processes and enhance overall efficiency.
 
Attendees will gain insights into:
 

1. Understanding the Neobank Ecosystem: We'll discuss the unique challenges and opportunities faced by neobanks, including the need for rapid innovation, regulatory compliance, and user-centric design.
2. Establishing a Robust QA Framework: Learn how to build a comprehensive QA framework tailored for neobanks, incorporating best practices for automated testing, and continuous integration.
3. Leveraging Automation and AI: Explore the latest tools and technologies that can automate testing processes, from functional testing to performance and security testing, thus reducing time-to-market while maintaining high standards.
4. Collaboration and Communication: Discover effective strategies for fostering collaboration between QA teams, developers, and other stakeholders, which is vital for ensuring seamless product development and early detection of issues.
5. Metrics and Continuous Improvement: Understand the importance of using metrics to measure QA effectiveness and drive continuous improvement in processes and team performance.
6. Case Studies and Real-World Examples: The talk will include case studies from our successful neobank Snappi that have implemented streamlined QA processes, sharing lessons learned and best practices.

 
Join us for this insightful session to learn how to effectively streamline your QA processes, enhance product quality, and deliver exceptional digital banking experiences that meet the demands of today’s consumers. Whether you're a QA professional, developer, or a leader in the fintech space, this talk will equip you with actionable strategies to thrive in the competitive neobank landscape.

Software testing is often seen as a technical gatekeeping step—but in reality, it’s a bridge between business goals, user needs, and data-driven insights. Drawing on my experience as a Test Analyst at the European Parliament and IT Consultant at Netcompany-Intrasoft, I’ll share how combining skills in statistical data analysis, economics, and quality assurance can create a richer testing practice. This talk will cover how testers can move beyond purely technical validation to become strategic contributors—spotting risks early, understanding business context, and improving the human experience of software. We’ll explore practical examples of merging manual testing, regression strategies, and user empathy to ensure quality that’s not just functional, but meaningful. Attendees will walk away with techniques for integrating data thinking into testing, fostering better collaboration with stakeholders, and ultimately delivering products that users trust.

You have kids, and suddenly, everything changes. Life starts revolving around them, and you feel like you're losing touch with the business world. Sound familiar?
I used to think that being a mom would hold me back in my testing career. But then I realized that many of the experiences I had at home could actually help me grow in the testing world. Cooking with the kids—or leaving them unattended—has led to unpredictable situations that taught me valuable lessons. And time spent at the playground with my twins? It helped me automate some surprisingly useful parenting skills—skills that turned out to be just as effective in testing environments.
Just as parents adapt to ever-changing situations and diverse needs, testers must navigate complex requirements and evolving user expectations. In this session, I’ll share stories and parallels between family moments and testing practices, exploring qualities like patience, multitasking, and problem-solving.
Whether you’re a parent or not, you’ll walk away with practical insights and maybe a laugh or two, ready to approach testing with fresh eyes and renewed creativity. Curious how chaos can lead to better software? Join me to find out!