Automated testing and fast feedback loops are widely recognized as essential to software development, yet many teams struggle to adopt and maintain effective testing practices. AI offers new opportunities to generate tests more quickly, but it also raises important questions about how those tests should be created, evaluated, and used.

This session explores the practical use of AI for creating automated tests. We will examine why test generation matters, when AI-assisted testing is most valuable, and how teams can incorporate AI-generated tests into their development workflows. The discussion will cover both the benefits and the limitations of relying on AI to produce test suites.

Using real-world examples and practical guidance, the talk will explore the role of automated tests in creating faster feedback loops, the differences across project types, and the challenges that arise when AI-generated tests are treated as authoritative without sufficient review. Attendees will leave with a clearer understanding of how to use AI as a tool for improving testing practices while maintaining confidence in the quality of the resulting tests.

What You Will Learn:

• Why AI-assisted test generation can help improve feedback loops in software development
• When AI-generated tests are most useful and where their limitations become apparent
• Practical approaches for evaluating and improving AI-generated tests

Who Should Attend:

• Software engineers
• Technical leads
• Test engineers
• Quality engineers
• Engineering managers
• Teams adopting AI-assisted development
• Developers interested in improving automated testing practices