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A genetic approach for random testing of database systems

Summary: Genetic, execution-feedback-guided test generation evolves query features to exercise rarely touched DBMS paths and avoid optimizer short-circuits. Experiments show improved code coverage and efficiency over pure random testing, validated in Microsoft SQL Server deployments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9534
Venue
VLDB
Year
2007
Pagerank
6.0259936e-05
Overall Rank
4,644 | 67.70%
DOI
-

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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
3,560 Counting, Enumerating, and Sampling of Execution Plans in a Cost-Based Query Optimizer 2000 SIGMOD 6.9723858e-05
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