Talks

How do you measure the real impact of GenAI in testing at the scale of 1,000+ QA engineers and separate actual value from the “wow” effect? We’ll share the metric framework and results from real implementations: LLMs for generating test documentation, accelerating E2E automation, and AI agents running regression tests.

Let's reflect and discuss new tools together!

Let's build a simple RAG bot based on Heisenbug conference program.

LLM-as-a-Judge in AI agent testing automation — let's discuss why this practice is vital, which models to choose, why LLM-as-a-Judge will deceive you with its simplicity, and how to start writing regression automation for your AI agents.

We will tell you what problems appeared, how we learned to reproduce the defect, how we stepped on a rake, and how it all ended (or did not end).

We will discuss how to implement AI in a company if you do not have large resources and free ML teams. I'll show you the free Roo Code AI assistant, as well as an unusual approach to writing feature evaluation agents, commit analysis, and requirements testing.

Testing the Smile Payment service.

Security check and search for biometric system vulnerabilities.

Errors occur at all stages of software development. The sooner we find out, the cheaper it is to fix them. At WS, testers will learn how to identify errors in requirements before the development stage. This will reduce the number of potential errors, the time and effort of the team to fix them.

This talk focuses on the practice of testing applications based on large language models (LLM) using the DeepEval tool. It will cover the automation of quality assessment using the LLM-as-a-Judge approach, specialized metrics, and the integration of testing into the development process to ensure the reliability and predictability of system operation.

I will tell you how to build a simple and convenient tool for measuring the energy consumption of various electrical appliances and integrate it into your infrastructure.