Step-wise Execution of Data-Centric Systems

Chi Zhang. 2024. SPLASH Companion.

Read the paper · doi:10.1145/3689491.3691821

What this paper does with SQLancer

NoREC and TLP are named as state-of-the-art approaches for detecting logic bugs in DBMSs, the line of work the step-wise reference construction is set against. This paper proposes a black-box methodology for testing data-centric systems, motivated by the observation that existing methods suffer from ineffective oracles, insufficient coverage and inefficient test generation. Its key idea is to build the test case incrementally and use the intermediate results to construct a reference case, whose result must match the original's. Applied to Datalog engines and DBMSs it uncovered 75 unique bugs. Written by claude-opus-5 from the 2 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 — yes

M1 calls NoREC, TLP and TQS all state-of-the-art approaches for detecting logic bugs in DBMSs.

NoREC [ 7], TLP [ 8], and TQS [ 9] are all stateof-the-art approaches for detecting logic bugs in DBMSs. M1 · 2 Problem · page 2

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.

#	Entry	Matched as
7	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
8	Manuel Rigger and Zhendong Su. 2020. Finding bugs in database systems via query partitioning. Proceedings of the ACM on Programming Languages 4, OOPSLA (2020), 1–30.	sqlancer publication · TLP
10	Chi Zhang, Linzhang Wang, and Manuel Rigger. 2024. Finding CrossRule Optimization Bugs in Datalog Engines. Proc. ACM Program. Lang. 8, OOPSLA1, Article 98 (apr 2024), 27 pages. 21	project authored

Every place it refers to SQLancer (2)

2 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	NoREC [ 7], TLP [ 8], and TQS [ 9] are all stateof-the-art approaches for detecting logic bugs in DBMSs.	technique state of the art	2 Problem page 2
M2	Fuzzing Datalog engines [ 10].	citation marker project authored background	4 Evaluation Methodology page 3

This page is rendered from _data/papers/paper_doi_10_1145_3689491_3691821.json, extracted from supplied pdf. 3 pages, 10 references parsed.