← Research building on SQLancer

Eslam Abdelkarim, Carsten Binnig, Anupam Sanghi. 2026. DBTest@SIGMOD.

Read the paper · doi:10.1145/3810991.3811637

What this paper does with SQLancer

SQLancer is the reference point for what coverage-oriented generation is being contrasted with: it is described as generating syntactically valid queries paired with logic-testing oracles such as PQS, NoREC and TLP. ShQveL, from the same project, is also cited for integrating LLM-generated fragments into an existing generator. Quover uses a large language model to generate SQL aimed at raising code coverage in DBMS testing. It works iteratively: it identifies functions in the DBMS source not yet covered, prompts the model with context including function descriptions and code snippets, generates targeted queries, and validates their effect. The authors report over 57% coverage within a few hours, a 14% improvement over the automated systems they compare with. 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 (5)

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
3 Manuel Rigger. 2022. SQLancer. https://github.com/sqlancer/sqlancer. Accessed 2026-02-25. sqlancer publication
4 Manuel Rigger and Zhendong Su. 2020. Detecting optimization bugs in database engines via non-optimizing reference engine construction. InProceedings of the 28th ACM Joint Meeting on European Software Engineering Confe... sqlancer publication · NOREC
5 Manuel Rigger and Zhendong Su. 2020. Finding bugs in database systems via query partitioning.Proc. ACM Program. Lang.4, OOPSLA, Article 211 (2020), 30 pages. doi:10.1145/3428279 sqlancer publication · TLP
6 Manuel Rigger and Zhendong Su. 2020. Testing database engines via pivoted query synthesis. InProceedings of the 14th USENIX Conference on Operating Systems Design and Implementation (OSDI’20). USENIX Association, Arti... sqlancer publication · PQS
13 Suyang Zhong and Manuel Rigger. 2025. Testing Database Systems with Large Language Model Synthesized Fragments. arXiv:2505.02012 [cs.SE] 53 sqlancer publication

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 SQL fuzzing tools such as SQLancer [3, 6] automatically generate syntactically valid queries and use logic-testing oracles (e. name
background 		1 Introduction
page 1
M2 , Pivoted Query Synthesis) to uncover logic bugs in DBMSs. technique
definition 		1 Introduction
page 1
M3 Logic-testing tools such as SQLancer [ 3,6] generate queries and pair them with metamorphic or partitioning-based oracles (e. name
background 		3.3 Ablation Study
page 4
M4 , Pivoted Query Synthesis (PQS) [ 6], NoREC [ 4], and Ternary Logic Partitioning (TLP) [ 5]) to uncover logic bugs and exercise diverse execution paths in DBMSs, but they do not explicitly optimize for exercising uncovered code regions. technique
definition 		3.3 Ablation Study
page 4
M5 ShQveL [ 13] integrates LLM-generated SQL fragments into an existing generator via “SQL sketching”, validating successful fragments and reusing them to increase feature coverage and uncover new DBMS bugs. name
background 		3.3 Ablation Study
page 4

This page is rendered from _data/papers/paper_doi_10_1145_3810991_3811637.json, extracted from supplied pdf. 5 pages, 13 references parsed.