Agenda

Have you ever been held “accountable” for quality or pressed for time, leaving you feeling helpless? Or perhaps you’ve been asked to justify quality assurance initiatives with numbers?

Our industry is filled with myths: from “quality can be measured” to “more testing means higher quality” and from “QA slows down work” to “testers are responsible for quality”. I believed them all too once, then I started questioning my beliefs when I saw how product quality improved when we reduced code coverage from 70% to 40% — I then realised that quality metrics are only proxy metrics and their correlation should be revisited regularly.

Join me and let's see what dangers all these myths pose to both teams and products, and how they should be debunked and overcome.

Fast forward to 2025, and the landscape has significantly transformed. AI now spits out well-structured, functional code in seconds. This shift raises a critical question: when AI can generate code effortlessly, what role remains for software craftsmanship? With AI tools automating many routine tasks, we’re being pushed to rethink long-standing practices like Test-Driven Development and Clean Code. This talk explores what truly matters for software crafters in the age of AI, focusing on the two most critical skills for staying relevant: testing and code review. We’ll dive into advanced techniques like mutation testing and property-based testing, powerful methods for testing and verifying AI-generated code. You’ll also discover which AI tools genuinely enhance software quality without replacing developer judgment. Finally, I’ll walk you through the most AI-friendly software architecture that pairs perfectly with acceptance testing and enables sustainable software development with AI.

Key takeaways:

● Do we still need TDD and Clean Code, or is “vibe coding” the new normal?

● The two most essential skills in the age of AI

● Property-based testing: how to write 1,000 tests with little effort for AI code

● Mutation testing in action: finding the gaps AI can’t see

● Practical AI tools that genuinely improve code quality

● The most AI-friendly software architecture and how to test it effectively