SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in
  Real-World Applications
Authors: name: Jinyang Li, name: Xiaolong Li, name: Ge Qu, name: Per Jacobsson, name: Bowen Qin, name: Binyuan Hui, name: Shuzheng Si, name: Nan Huo, name: Xiaohan Xu, name: Yue Zhang, name: Ziwei Tang, name: Yuanshuai Li, name: Florensia Widjaja, name: Xintong Zhu, name: Feige Zhou, name: Yongfeng Huang, name: Yannis Papakonstantinou, name: Fatma Ozcan, name: Chenhao Ma, name: Reynold Cheng

Resolution of complex SQL issues persists as a significant bottleneck in
real-world database applications. Current Large Language Models (LLMs), while
adept at text-to-SQL translation, have not been rigorously evaluated on the
more challenging task of debugging SQL issues. To address this gap, we
introduce BIRD-CRITIC, a new SQL issue debugging benchmark comprising 530
PostgreSQL tasks (BIRD-CRITIC-PG) and 570 multi-dialect tasks
(BIRD-CRITIC-Multi), distilled from authentic user issues and replayed within
new environments to facilitate rigorous evaluation. Baseline evaluations
underscore the task's complexity, with the leading reasoning model O3-Mini
achieving only 38.87% success rate on BIRD-CRITIC-PG and 33.33% on
BIRD-CRITIC-Multi. Meanwhile, advancing open-source models for database tasks
is crucial for empowering local development while safeguarding data privacy.
Therefore, we present Six-Gym (Sql-fIX-Gym), a training environment for
elevating open-source model capabilities for SQL issue debugging. This
environment leverages SQL-Rewind strategy, which automatically generates
executable issue-solution datasets by reverse-engineering issues from verified
SQLs. However, popular trajectory-based fine-tuning methods do not explore
substantial supervisory signals. We further propose f-Plan Boosting, which
extracts high-level debugging plans from SQL solutions, enabling teacher LLMs
to produce 73.7% more successful trajectories for training. We integrate these
components into an open-source agent, Bird-Fixer. Based on Qwen-2.5-Coder-14B,
Bird-Fixer achieves 38.11% success rate on BIRD-CRITIC-PG and 29.65% on
BIRD-CRITIC-Multi, surpassing leading proprietary models such as
Claude-3.7-Sonnet and GPT-4.1, marking a significant step toward democratizing
sophisticated SQL-debugging capabilities. The leaderboard and source code are
available: https://bird-critic.github.io/