Vehicle software changes every day and even correct updates can cause runtime performance to degrade, yet manually analysing logs to trace a failed test or creeping performance issue to the responsible commit or function still takes days. This talk describes an approach that has been validated in a production Tier 1 HIL environment, and it connects code changes, builds, HIL rigs, test runs and decoded CAN, LIN and Automotive Ethernet traffic into one traceable chain, providing automated anomaly detection, drill‑down investigation and grounded root‑cause reports that link failures to commits in seconds, and the same pipeline scales to test fleets

• Why manual log analysis breaks at software-defined-vehicle build cadence — and what a measurement-data platform for the test floor looks like
• How open data formats turn terabytes of CAN, LIN and Automotive Ethernet logs into queryable test data. On-premise, with answers in seconds
• How automated anomaly detection enables review by exception - engineers see only new or changed findings, not millions of raw frames
• How one traceable chain - code change, build, HIL rig, test, analysis - links each failure or performance drop to the responsible commit
• What a grounded on-premise AI copilot can do for root cause — written, fact-checked reports with zero data leaving the test network