From 185 Failures to One Answer: Building AI-Native Test Intelligence for Embedded Hardware-in-the-Loop Testing

When a nightly CI run produces 185 failures across embedded hardware-in-the-loop tests, how do you find the one root cause? Existing test analytics platforms — built for web teams — have no concept of physical devices, boot cycles, or firmware services.

This talk presents a production-validated test intelligence platform for embedded CI/CD. Running at a major European consumer electronics company across thousands of CI runs, it provides: failure clustering reducing hundreds of failures to actionable root causes; a learned pattern library classifying failures by source (infrastructure, firmware, test); DUT health tracking flagging hardware-suspect devices; boot cycle analysis detecting watchdog resets and kernel panics; and persistent engineering memory preserving debugging insights across sessions and team members.

Implemented as a 90+ tool MCP server, engineers query test intelligence directly from their AI coding assistant. The talk includes live demos with real CI data and practical architecture patterns for embedded teams.