Modern software-defined vehicle programmes generate vast numbers of defect tickets and trace logs. Critical engineering decisions are often slowed by manual pre-analysis, fragmented toolchains, and reliance on individual experts. In a typical OEM setting, error management teams handle ~300 new tickets per week, yet up to 40% cannot be analysed immediately due to missing information, causing delays and rework. Engineers manually locate trace files, convert binary logs, search for patterns, and copy findings into downstream systems.

This presentation introduces a data-driven AI approach that automates large parts of this workflow. A scalable pipeline ingests ticket and trace data to verify ticket quality, detect duplicates, generate concise summaries, and perform trace-centric root-cause analysis. The key innovation is the tight integration of ticket management and trace analysis in a single pipeline, enabling consistent, repeatable investigations beyond isolated manual efforts. For trace investigations, the system identifies relevant files from large repositories, converts binary traces into machine-readable form, validates data integrity, applies pattern matching, and produces human-readable diagnostics using a large language model (LLM). It also visualises error occurrences and system state transitions to pinpoint fault contexts. Beyond pattern matching, an LLM-based agent autonomously orchestrates investigations by querying signals, cross-referencing ticket context, and iterating towards root-cause hypotheses without predefined rules.

Impact: The approach improves efficiency and decision-making in SDV development. It streamlines ticket creation and analysis for testers and function owners and enables project engineers to perform deep-dive investigations without expert intervention. In production use, initial results indicate up to an 80% reduction in trace analysis effort per ticket, with improved consistency and accessibility for non-experts. Overall, AI-driven automation turns large-scale validation traces into faster root-cause hypotheses, quicker duplicate detection, and more robust ticket quality management, without relying on proprietary tools or data.

Keypoints: 

• AI- and data-driven framework for automating ticket and trace analysis in SDV validation.
• Unified, scalable pipeline for ticket quality checks, duplicate detection, summarisation, and trace-centric investigation.
• End-to-end automated trace processing, including file selection, conversion, validation, and pattern-based analysis.
• LLM-supported summaries and visual analytics to accelerate root-cause hypothesis generation and improve accessibility for non-experts
• Measured operational impact, with improved consistency and up to 80% reduction in trace analysis effort per ticket.