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Effective Bug Detection in Graph Database Engines: An LLM-based Approach

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arxiv 2402.00292 v1 pith:HPQ5BMD7 submitted 2024-02-01 cs.DB

Jiayi Wu , Zhengyu Wu , Ronghua Li , Hongchao Qin , Guoren Wang This is my paper · ORCID
classification cs.DB
keywords graphdatabaseenginesquerybugslanguagelanguagesqueries
verification ladder T0 review T1 audit T2 compute T3 formal
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Graph database engines play a pivotal role in efficiently storing and managing graph data across various domains, including bioinformatics, knowledge graphs, and recommender systems. Ensuring data accuracy within graph database engines is paramount, as inaccuracies can yield unreliable analytical outcomes. Current bug-detection approaches are confined to specific graph query languages, limiting their applicabilities when handling graph database engines that use various graph query languages across various domains. Moreover, they require extensive prior knowledge to generate queries for detecting bugs. To address these challenges, we introduces DGDB, a novel paradigm harnessing large language models(LLM), such as ChatGPT, for comprehensive bug detection in graph database engines. DGDB leverages ChatGPT to generate high-quality queries for different graph query languages. It subsequently employs differential testing to identify bugs in graph database engines. We applied this paradigm to graph database engines using the Gremlin query language and those using the Cypher query language, generating approximately 4,000 queries each. In the latest versions of Neo4j, Agensgraph, and JanusGraph databases, we detected 2, 5, and 3 wrong-result bugs, respectively.

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Cited by 2 Pith papers

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  1. A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models

    cs.IR 2025-07 conditional novelty 5.0 of 10

    QMKGF builds multi-path knowledge graph subgraphs from LLM-extracted entities, fuses the highest-scoring subgraph with query-relevant triples, and expands the query to improve RAG answer quality.

  2. Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets

    cs.IR 2025-05 conditional novelty 5.0 of 10

    A curation pipeline with a shared data model, a repository, a knowledge graph, and a ChatGPT-based chatbot that lets researchers ask questions across human-robot datasets.

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