# SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting

arXiv: https://arxiv.org/abs/2508.18554 · HTML: https://mlsanity.com/paper/2508.18554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [UniParser: A Unified Log Parser for Heterogeneous Log Data](https://mlsanity.com/paper/2202.06569/) (2022)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)

## Built on by (most cited first, 2 of 2)

- [FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification](https://mlsanity.com/paper/2604.03245/) (2026)
- [SCALE: Self-Supervised Constraint-Aware Layout GEneration for Local P&R DRV Fixing at Advanced Nodes](https://mlsanity.com/paper/2607.21850/) (2026)

## Abstract (arXiv)

Log schema extraction is the process of deriving human-readable templates from massive volumes of log data, which is essential yet notoriously labor-intensive. Recent studies have attempted to streamline this task by leveraging Large Language Models (LLMs) for automated schema extraction. However, existing methods invariably rely on predefined regular expressions, necessitating human domain expertise and severely limiting productivity gains. To fundamentally address this limitation, we introduce SchemaCoder, the first fully automated schema extraction framework applicable to a wide range of log file formats without requiring human customization within the flow. At its core, SchemaCoder features a novel Residual Question-Tree (Q-Tree) Boosting mechanism that iteratively refines schema extraction through targeted, adaptive queries driven by LLMs. Particularly, our method partitions logs into semantic chunks via context-bounded segmentation, selects representative patterns using embedding-based sampling, and generates schema code through hierarchical Q-Tree-driven LLM queries, iteratively refined by our textual-residual evolutionary optimizer and residual boosting. Experimental validation demonstrates SchemaCoder's superiority on the widely-used LogHub-2.0 benchmark, achieving an average improvement of 21.3% over state-of-the-arts.