← Research building on SQLancer

Jagan Ankam. 2022. International Journal of Computational and Experimental Science and Engineering.

Read the paper · doi:10.22399/ijcesen.5462

What this paper does with SQLancer

All three SQLancer oracles are described by name as the database-testing precedent: PQS constructing queries expected to retrieve a selected row, NoREC comparing an optimizable query with a non-optimizable equivalent, TLP checking relations among partitioned results. The paper distinguishes them as targeting engine defects rather than the application-level transformations it studies. A benchmark and taxonomy for human review of AI-generated data transformations. The premise is that generated SQL, Spark and dbt code can compile, run and preserve the expected schema while silently changing what downstream data means. Across 12 transformation tasks the authors evaluate three automated checkpoint families, finding that comparison against a trusted baseline catches most defects while schema checks and data-quality rules catch few, and that one residual defect is found only by reviewing logic and business intent together. Written by claude-opus-5 from the 5 places this paper refers to SQLancer. The quotations below are the paper's own words, stored verbatim when the text was extracted.

How it was classified

uses infrastructure — no

SQLancer is cited, not used; nothing in the mentions describes reusing its code.

extends technique — no

No technique is extended; the citation is background.

compares with — no

No empirical comparison against SQLancer is reported in the mentions.

describes as state of the art — no

The citation does not characterise SQLancer as the state of the art.

SQLancer publications it cites (3)

Bibliography entries that resolved to a SQLancer publication, or to a paper by one of the project's authors. A sentence citing one of these numbers is a reference to SQLancer even when it never writes the name.

#EntryMatched as
17 M. Rigger and Z. Su, “Testing database engines via pivoted query synthesis,” in Proc. 14th USENIX Symp. Oper. Syst. Design Implement., 2020, pp. 667–682, doi: 10.48550/arXiv.2001.04174. sqlancer publication · PQS
18 M. Rigger and Z. Su, “Detecting optimization bugs in database engines via non -optimizing reference engine construction,” in Proc. 28th ACM ESEC/FSE, 2020, pp. 1140 –1152, doi: 10.1145/3368089.3409710. sqlancer publication · NOREC
19 M. Rigger and Z. Su, “Finding bugs in database systems via query partitioning,” Proc. ACM Program. Lang., vol. 4, OOPSLA, Art. no. 211, 2020, doi: 10.1145/3428279. sqlancer publication · TLP

Every place it refers to SQLancer (5)

5 sentences, each stored verbatim from the extracted text with where it was found and how. “Citation marker” means the sentence names no tool at all and was reached through a reference number that resolved to a SQLancer publication.

Id Sentence Found by Where
M1 Database testing research develops oracles for defects in database engines rather than defects in applicati on-level transformation intent [17] –[21]. citation marker
motivation 		1 Introduction
page 3
M2 Pivoted Query Synthesis constructs queries expected to retrieve a selected row [17]. technique
definition 		3 A task -level comparison of schema checks,
page 4
M3 NoREC compare s an optimizable query with an equivalent form intended to suppress optimization [18]. technique
definition 		3 A task -level comparison of schema checks,
page 4
M4 Ternary Logic Partitioning checks relations among partitioned query results [19]. technique
definition 		3 A task -level comparison of schema checks,
page 4
M5 Database differential testing [17], [18], [19] Equivalent or partitioned query results Yes Adjudicates divergent executions Targets DBMS implementation rather than business intent. citation marker
background 		7 Use Level 4 release controls: canary runs,
page 19

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