Detecting Logic Bugs in DBMSs via Equivalent Data Construction
Summary: Presents Equivalent Data Construction (EDC): precompute expression results, rewrite queries to replace operations with those constants, and report mismatches to detect implementation-level bugs in data operations (coercion/overflow/precision). Applied to 7 DBMSs, EDC uncovered 54 previously unknown bugs (39 confirmed), triggering developer engagement. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Wenqian Deng
- 2. Jie Liang
- 3. Zhiyong Wu
- 4. Jingzhou Fu
- 5. Yu Jiang
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 406 | Massive Stochastic Testing of SQL | 1998 | VLDB | 0.00024053686 |
| 488 | TiDB: A Raft-based HTAP Database | 2020 | VLDB | 0.000220409 |
| 2,650 | Detecting Logic Bugs of Join Optimizations in DBMS | 2023 | SIGMOD | 8.3708191e-05 |
| 5,942 | SAM: Database Generation from Query Workloads with Supervised Autoregressive Models | 2022 | SIGMOD | 5.2634242e-05 |
| 6,137 | Detecting Metadata-Related Logic Bugs in Database Systems via Raw Database Construction | 2024 | VLDB | 5.1916986e-05 |
| 7,895 | HYDRA: A Dynamic Big Data Regenerator | 2018 | VLDB | 4.623701e-05 |
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