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New benchmark ProcArena reveals LLM struggles with PL/SQL code generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ProcArena, a new benchmark designed to evaluate large language models (LLMs) on their ability to generate PL/SQL code from natural language. The benchmark covers both direct generation and interactive development scenarios across PostgreSQL and Oracle databases, encompassing tasks like initial development, code modification, debugging, and optimization. Initial evaluations of seven LLMs revealed that the best-performing models achieved only 62.2% and 57.8% accuracy in direct and interactive modes, respectively, highlighting the significant challenges that remain in realistic NL-to-PL/SQL development. AI

IMPACT Highlights current limitations of LLMs in complex code generation tasks, suggesting areas for future research and development.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark ProcArena reveals LLM struggles with PL/SQL code generation

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The cluster contains a research paper introducing a new benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hang Zhang, Chaokun Wang, Yuzhi Pan, Ziyao Zhong, Shuo Cao, Yue Xue, Zeyu Huang, Xingwei Zhou, Fang Niu, Bofan Xie, Guanchen Ge, Leqi Zheng, Ziyang Liu, Xiannian Cao, Pengcheng Ge ·

    ProcArena: A Multi-Scenario Benchmark for LLMs on Direct and Interactive PL/SQL Development from Natural Language

    arXiv:2609.06527v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for translating natural-language (NL) requirements into PL/SQL programs, attracting increasing attention from the database community. However, existing NL-to-PL/SQL efforts …

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