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ParSEval: Plan-aware Test Database Generation for SQL Equivalence Evaluation

Summary: ParSEval generates SQL test databases using operator semantics and branch coverage over logical-plan execution paths, reducing false positives in equivalence testing. It supports 40% more query pairs than verifiers and finds more inequivalences than prior generators, 21× faster. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14270
Venue
VLDB
Year
2025
Pagerank
5.1915905e-05
Overall Rank
9,945 | 31.77%
DOI
10.14778/3749646.3749727

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chen_vldb25,
        title = {{ParSEval: Plan-aware Test Database Generation for SQL Equivalence Evaluation}},
        author = {Chen, Chunyu and Miao, Zhengjie and Zhang, Yong and Wang, Jiannan},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4750--4762},
        doi = {10.14778/3749646.3749727},
        url = {https://doi.org/10.14778/3749646.3749727},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,199 Automated Discovery of Test Oracles for Database Management Systems Using LLMs 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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