Schedule

The results of the TechRadar survey.

Using the example of a real multi-agent autotesting system, we show how choosing the right model for a task and several engineering techniques result in 40-90% savings without loss of quality.

What should be the architecture of an autotest framework for the backend? Let's discuss proven solutions, scalability, and practical experience.

I will share a practical case study of Data Quality implementation in a tax reporting project, where the synergy of PyDeequ and Soda Core helped stabilize calculations and prevent financial risks. Additionally, I will showcase our custom DQ-generator, which transforms metadata into ready-to-use validation rules and significantly reduces engineers' routine work.

TechRadar shows what is happening in the industry. And we offer to understand together — why exactly so and what it means for our work. After the presentation of the study, we invite you to discuss the most interesting results at the round tables together with experts and colleagues. We will collect and present the main conclusions and insights of the participants at the closing of the conference.

How to automate the analysis of automated test failures after CI/CD runs without turning this process into “we’ve handed everything over to neural networks”?

The quality of medical AI cannot be measured by a single offline metric — the error may lie in the data, configuration, preprocessing, integration, or infrastructure of the client. Using real files, I'll show you how we test the entire research path and why we consider monitoring to be part of testing.

How to find bugs not in applications, but in the QA tools themselves, which we trust to provide testing results.

At the workshop, we will build our own QAI agent, an agent system that helps us test tasks and perform root-cause analysis.

A real story of how my team went from 30-40 minutes per integration test run down to 3-4 minutes. We'll talk about how we got there, the mistakes we made along the way, and where we ended up.

How to test autonomous multi-agent systems if their quality is not deterministic and errors can hide deep within the chain of agents?

Let’s talk about how to assess real‑world conditions, identify dependencies and limitations, and make decisions that help the team create a system that meets the market needs.

How to flexibly and painlessly automate CLI testing using FW as an example.

Let's trace how the approaches to testing quality assessment have evolved: from line coverage and data coverage to fuzzing, context coverage, and business coverage.

Using small, visual examples, we will explore typical "coverage illusions" (green strings and increasing percentages that do not improve the quality of tests), show how cyclomatic complexity can set a lower bound on the number of tests required, and discuss mutation testing as an additional approach and its practical limitations.

We will tell you how we test a delivery robot and use examples to show how the testing strategy changes at different stages of the product lifecycle.

As the pywinauto maintainer, I talk about cross-platform desktop UI tests, about code (DLL/SO) injection into UI process which should challenge commercial tools, and explain few complicated cases.

At the workshop, we will discuss how modern agent systems are structured and what techniques are used to test them for security.

We will tell you how we run end-to-end tests on production code at Avito without a full E2E environment, isolating external dependencies using fakes.

What is field testing of telecom equipment and why is it needed in a real network?

Agents in QA work: from bug analysis to quality gate in pipeline. What is hidden behind each setting, how much does one dialogue move cost, and why do the Playwright authors advise against using their own MCP.

Using practical examples, we will discuss how to review the test model, find blind spots and focus on checks that really reduce the risk of defects in production.

Let's discuss the results of modern psychological research and find out why critical thinking is becoming especially important in the age of AI.

Summing up the results of the conference, remembering the highlights and talking about plans.