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AGENDA

  • 07:00pm CET - Welcome, Rudolf Grötz
  • 07:05pm - Turning Production Signals into Test Results - Exploratory Testing Supported by AI, Benjamin Hofmann
  • 07:45pm - Q&A
  • 08:00pm Call it the Day

Turning Production Signals into Test Results - Exploratory Testing Supported by AI

Pre-production testing helps reduce known risks. Testing in production helps uncover unknown risks.

These unknowns only appear when real users, real data and real traffic hit the system. That is why monitoring is the execution layer of Testing in Production.

In production, anything that behaves differently than expected is valuable feedback. Incidents are test results under real conditions, just like anomalies and subtle changes that never turn into full outages. All of them show how the system actually behaves.

This makes Testing in Production a form of exploratory testing. You do not follow predefined test cases. You explore what happens when reality meets the system.

Most often, the problem is not a lack of signals. Teams already collect logs, metrics and alerts. The problem is turning those signals into understanding fast enough.

This is where Hyground comes in.

Hyground elevates learning from production by allowing teams to actively explore production signals. It correlates logs, metrics, events and alerts into coherent investigation paths and makes the system behavior explainable without requiring deep operational expertise.

In doing so, Hyground democratises access to the most realistic exploratory test environment there is: your running production system. Testers can follow anomalies, ask questions, validate hypotheses and learn from real behavior, not just from predefined checks.

Testing in production is already happening. Hyground turns it into a deliberate, exploratory practice instead of an accidental one.

In this spotlight, you will learn:

About Hyground and how it already supports operations in investigations
How operational incident analysis aligns with exploratory testing in production
How testers can explore production behavior without deep ops expertise
How production signals become structured, explainable feedback

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