← Research building on SQLancer

Shihao Wen, Peng Jia, Pin Yang, Chi Hu. 2023. Applied Sciences.

Read the paper · doi:10.3390/app13042519

What this paper does with SQLancer

SQLancer is named as a leading example of generation-based DBMS fuzzing and described as constructing functionally equivalent SQL statements through several patterns and feeding them to the same DBMS. The paper is explicit that it does not compare against it: SQLancer and SQLRight target logic bugs, while Squill, like Squirrel, targets memory errors, so the two are measured on different things. Squill fuzzes DBMSs for memory errors, using correctness feedback to keep generated statements valid and accurate instantiation to fill in values that satisfy the schema. Valid statements reach deeper into the engine, which is where memory errors are found. Written by claude-opus-5 from the 3 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 as related work; no reuse of its code, generator or workload is described.

extends technique — no

No SQLancer technique is extended; the citation is background.

compares with — no

M2 states directly that Squill was not compared with SQLancer, because SQLancer targets logic bugs while Squill targets memory errors.

describes as state of the art — no

The text does not describe 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
2 Rigger, M.; Su, Z. Detecting Optimization Bugs in Database Engines via Non-Optimizing Reference Engine Construction. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposi... sqlancer publication · NOREC
3 Rigger, M.; Su, Z. Testing Database Engines via Pivoted Query Synthesis. In Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation, OSDI’20, Virtual, 4–6 November 2020; USENIX Associa... sqlancer publication · PQS
4 Rigger, M.; Su, Z. Finding Bugs in Database Systems via Query Partitioning. Proc. ACM Program. Lang. 2020 ,4, 1–30. [CrossRef] sqlancer publication · TLP

Every place it refers to SQLancer (3)

3 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 Black-box fuzzing, or generation-based fuzzing, has been extensively used in finding DBMS bugs, such as SQLsmith [1] and SQLancer [2–4]. name
background 		1 Introduction
page 1
M2 We did not compare Squill with SQLRight [ 18] and SQLancer, because their target is the logic bug of DBMSs, while Squill, like Squirrel, focuses on the memory error of DBMSs. name
incidental 		5 Evaluation
page 14
M3 Sqlancer [ 2–4] constructs different SQL statements of functionally equivalent through several different patterns and inputs them into the same DBMS. name
definition 		7 Related Work
page 21

This page is rendered from _data/papers/paper_doi_10_3390_app13042519.json, extracted from supplied pdf. 23 pages, 43 references parsed.