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PLSQLBench: Benchmarking LLM Systems for Executable Procedural Database Programming

arXiv 2608.15931 ↗First posted 16 Aug 2026cs.CLcited by 0

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Abstract (arXiv)

We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured through execution-based tests. Existing LLM evaluations largely target general-purpose code generation or declarative text-to-SQL, leaving procedural database programming underexplored. PLSQLBench contains 2,865 instances: 2,594 single-turn tasks and 271 multi-turn conversations spanning 978 turns. The benchmark combines complex schema-grounded tasks over enterprise-style Spider 2 databases, simpler schema-grounded tasks derived from Spider, and MBPP-derived procedural problems, covering varying levels of database grounding and procedural complexity. Experiments with eight LLMs reveal recurring difficulties in schema grounding, PL/SQL dialect fidelity, procedural control flow, exception handling, and cross-turn consistency. Tool-augmented LLM agents improve performance on several schema-grounded evaluations, although substantial gaps remain. These results highlight procedural database programming capabilities not directly assessed by conventional code generation or text-to-SQL benchmarks. Our code is available at https://github.com/oracle-samples/plsqlbench.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 16 Aug 2026.

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References · 17

  1. 2021Program Synthesis with Large Language Modelscited by 3,637
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
  4. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  7. 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 546
  8. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 326
  9. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 185
  10. 2022Multi-lingual Evaluation of Code Generation Modelscited by 159
  11. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 144
  12. 2024WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Taskscited by 90
5 more
  1. 2024Evaluating Language Models for Efficient Code Generationcited by 73
  2. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 42
  3. 2025Benchmarking Deep Search over Heterogeneous Enterprise Datacited by 23
  4. 2025BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactionscited by 19
  5. 2025OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoningcited by 3

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