Systems | Development | Analytics | API | Testing

Katalon supports the full testing lifecycle by using Studio to create tests, TrueTest to generate additional user-based tests, and TestOps to manage everything: from organizing, planning, and executing tests to analyzing results and tracking defects. Together, they ensure tests scale across teams and projects. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

Generative AI can produce tests instantly, but speed alone does not guarantee quality or safety. Without proper validation, AI-written tests can become brittle, redundant, or misleading, creating a false sense of coverage. This session looks at the risks of relying on AI-generated tests without the right controls in place.

Welcome to a new era of intelligent test automation with Katalon TrueTest — a revolutionary AI-powered solution that bridges the gap between manual and automated testing. In this detailed end-to-end walkthrough, Mahtab Siddique, Senior Solutions Architect at Katalon, showcases how TrueTest uses AI and real user behaviour to generate, maintain, and optimise automation tests automatically.

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.

Leadership today is facing a serious gap. Many people step into leadership and management roles without a clear understanding of how to lead, manage, or genuinely support their teams. While the fundamentals of leadership are simple, poor leadership makes it necessary to revisit the basics. Drawing from experience as a consultant, manager, individual contributor, and leader, this session focuses on what truly works and what consistently fails.

In machine learning, an 85% accurate model is often considered a success because we accept that data is messy, the real world is unpredictable, and chasing perfection is rarely worth the cost. However, in software testing, especially in the GenAI era, the question of “Can we test 100%?” still comes up. With AI systems producing probabilistic outputs where the same input can lead to different results, absolute coverage is unrealistic. Confidence scores already tell us that uncertainty is part of the system, and testing needs to acknowledge that reality.