Journal of Applied Science and Engineering

Published by Tamkang University Press

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SLQ: Lightweight-Detection & LLM-Generation SQL SelfOptimization Framework

 2026

 Volume 30

Zhu Tianyou, Qi Yaru, Jiang Kongchen, Sang Yanting, and Yang Chao

State Grid Information & Telecommunication center (Big Data center), Beijing 100052, P. R. China

Received: October 13, 2025
Accepted: November 16, 2025
Publication Date: March 8, 2026

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Schematic Diagram of the SLQ Framework.

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In real-world database applications, SQL statements written by users often create performance bottlenecks because they violate best-practice rules. Traditional rule-based detectors have limited ability to recognize diverse and increasingly irregular statements and are costly to maintain. To address this, we propose SLQ, a two-stage intelligent SQL optimization framework. First, a lightweight stacked-LSTM module pinpoints problematic statements; then a pre-trained large language model, Qwen3, automatically generates explanations for each flaw and offers targeted rewrite suggestions, helping users quickly improve query quality. Evaluated on a standard dataset, SLQ achieves accuracy, precision, recall and F1 of 0.9841, 0.9974, 0.9702 and 0.9836 respectively, demonstrating superior detection and optimization capability and markedly enhancing SQL compliance and execution efficiency.

Keywords: SQL Query Optimization; Large Language Model (LLM); Long Short-Term Memory(LSTM)

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