Presentation
Schemacoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting
DescriptionLog schema extraction, the process of deriving human-readable templates that support clear log understanding from massive volumes of log data, is fundamental for automated debugging and performance analysis across modern Electronic Design Automation (EDA) tool chains. The challenge is amplified in advanced design flows, where logs interweave command scripts, multi-stage progress reports, timing updates, and deeply nested performance tables, rendering manual regular-expression design brittle and unscalable to evolving tool versions. We introduce SchemaCoder, a fully-automated LLM-driven schema extraction framework that constructs structured log schemas by synthesizing reusable parser code, enabling robust handling of arbitrary unstructured log files and complex EDA tool logs without manual regular-expression design. At its core, SchemaCoder uses a novel Question-Tree (Q-Tree) pattern code generation process to identify pattern codes and utilizes the extracted raw contents to drive a textual residual evolutionary optimizer in the inner loop without relying on a gold labeled dataset. In the outer loop, a residual Q-Tree boosting mechanism identifies additional pattern codes and iteratively refines the parser code. On EDA tool logs from OpenROAD and commercial tools, the structured log schema generated by SchemaCoder enables an average 11.7% improvement in agentic EDA tool log analysis QA tasks (pass@1) over a strong commercial baseline. SchemaCoder also achieves up to 7.3% improvement in the average scores and outperforms state-of-the-art baselines in 9 of 14 applications on LogHub-2.0. We will open-source the code and the EDA tool log QA benchmark for reproducibility upon acceptance.
Event Type
Research Manuscript
TimeMonday, July 272:47pm - 3:00pm PDT
LocationMtg Room 101A