DBugScribe: Automatic Database Bug Reproduction from Community Reports
Read the paper · doi:10.1145/3802034
What this paper does with SQLancer
SQLancer appears in two roles, neither of them reuse. As background it is one of the automated generators whose test scenarios the paper calls transient -- the contrast that motivates working from durable community reports instead. More substantially, tools descended from SQLancer supply the evaluation corpus: DQP and CODDTest are among the eight tools whose confirmed bug reports DBugScribe reproduces, and one reproduced bug is noted as found by DQP and marked critical by developers. PQS, TLP and NoREC are cited as the oracle line that recent optimizer-centric work specialises.
DBugScribe reproduces database bugs automatically from the reports people file. Bug reports carry the setup, the query and the symptom in prose and fragments rather than as a runnable script, so reproducing one is manual work; DBugScribe parses a report into a domain-specific language describing the scenario and executes it. Its dataset is drawn from bugs that eight recent testing tools reported in widely used open-source relational DBMSs.
How it was classified
uses infrastructure — no
What DBugScribe consumes is the bug reports these tools produced, as evaluation data. That is not reuse of SQLancer's code, its generator or its workload.
extends technique — no
The contribution is reproducing reported bugs from prose, which shares no mechanism with any SQLancer oracle.
compares with — no
The tools supply the corpus of bugs to reproduce rather than serving as baselines; no run of SQLancer or its oracles is reported against DBugScribe.
describes as state of the art — no
SQLancer is described as providing automated test generation whose scenarios are transient, which is a limitation rather than a standing claim.
SQLancer publications it cites (14)
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 |
|---|---|---|
| 3 | Jinsheng Ba and Manuel Rigger. 2023. Testing Database Engines via Query Plan Guidance. InProceedings of the 45th International Conference on Software Engineering(Melbourne, Victoria, Australia)(ICSE ’23). IEEE Press, ... | sqlancer publication · QPG |
| 4 | Jinsheng Ba and Manuel Rigger. 2024. CERT: Finding Performance Issues in Database Systems Through the Lens of Cardinality Estimation. InProceedings of the IEEE/ACM 46th International Conference on Software Engineering... | sqlancer publication · CERT |
| 5 | Jinsheng Ba and Manuel Rigger. 2024. Keep It Simple: Testing Databases via Differential Query Plans. | sqlancer publication · DQP |
| 9 | Jack Clark, Alastair F. Donaldson, John Wickerson, and Manuel Rigger. 2024. Validating Database System Isolation Level Implementations with Version Certificate Recovery. InProceedings of the Nineteenth European Confer... | project authored |
| 11 | Wenjing Deng, Qiuyang Mang, Chengyu Zhang, and Manuel Rigger. 2024. Finding Logic Bugs in Spatial Database Engines via Affine Equivalent Inputs. 2024), 26 pages. | project authored |
| 13 | Jingzhou Fu, Jie Liang, Zhiyong Wu, Yanyang Zhao, Shanshan Li, and Yu Jiang. 2025. Understanding and Detecting SQL Function Bugs: Using Simple Boundary Arguments to Trigger Hundreds of DBMS Bugs. InProceedings of the ... | project authored |
| 21 | Zu-Ming Jiang, Si Liu, Manuel Rigger, and Zhendong Su. 2023. Detecting Transactional Bugs in Database Engines via Graph-Based Oracle Construction. In17th USENIX Symposium on Operating Systems Design and Implementation... | project authored |
| 36 | Qiuyang Mang, Jinsheng Ba, Pinjia He, and Manuel Rigger. 2025. Finding Logic Bugs in Graph-processing Systems via Graph-cutting. | project authored |
| 41 | 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 |
| 42 | Manuel Rigger and Zhendong Su. 2020. Finding bugs in database systems via query partitioning.Proc. ACM Program. Lang.4, OOPSLA, Article 211 (nov 2020), 30 pages. | sqlancer publication · TLP |
| 43 | Manuel Rigger and Zhendong Su. 2020. Testing database engines via pivoted query synthesis. In14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 667–682. | sqlancer publication · PQS |
| 63 | Chi Zhang and Manuel Rigger. 2025. Constant Optimization Driven Database System Testing. 2025), 24 pages. | sqlancer publication · CODDTEST |
| 67 | Suyang Zhong and Manuel Rigger. 2024. Understanding and Reusing Test Suites Across Database Systems. 2024), 26 pages. | project authored |
| 68 | Suyang Zhong and Manuel Rigger. 2026. Scaling Automated Database System Testing. InProceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume ... | sqlancer publication |
Every place it refers to SQLancer (15)
15 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 | This inherent complexity makes them prone to various categories of defects, including logic bugs [ 3,41–43,48,49,63] and crash bugs [ 20,46,66]. |
citation marker |
1 Introduction page 2 |
| M2 | While recent bug-detection tools analyze large corpora of DBMS bugs [ 10,13,54], they primarily focus on manual categorization and statistical characterization rather than automated reproduction. |
citation marker project authored |
1 Introduction page 2 |
| M3 | Prior work [ 67] has demonstrated the potential for cross-DBMS test reuse. |
citation marker project authored |
2.1 Problem Formulation page 4 |
| M4 | SQuaLity [ 67] represents an initial attempt to address the test reuse problem; however, its ability is limited, as it is hard to automatically 4, No. |
citation marker project authored |
3.3 DBugScribe DSL: DBMS Bug Scenarios page 6 |
| M5 | , SQLsmith [ 46] and SQLancer [ 41–43]) provide automation test generation in bug discovery, but the generated scenarios are transient. |
name |
3.3 DBugScribe DSL: DBMS Bug Scenarios page 7 |
| M6 | DBugScribe: Automatic Database Bug Reproduction from Community Reports 157:13 TiDB MySQLMariaDBCombdb2SQLite Clickhouse CockroachDBPostgreSQLDuckDBPolarDB Oceanbase0153045607590Reported bugs78 6 1 3 1321 1 1 LEGO[31] (ICDE ’23) Pinolo[18] (ATC ’23) TQS[51] (SIGMOD ’23) DQP[5] (SIGMOD ’24) EET[22] (OSDI ’24) Radar[48... |
technique |
4.1 Dataset Construction and Curation page 12 |
| M7 | Applying the above criteria, we identified in total eight recent tools [ 5,18,22, 31,48,49,51,63] that reported confirmed bugs in widely used open-source relational DBMSs. |
citation marker |
4.1 Dataset Construction and Curation page 13 |
| M8 | Among surveyed papers, we excluded three tools [ 11,36,71] that aim for NoSQL systems (e. |
citation marker project authored |
4.1 Dataset Construction and Curation page 13 |
| M9 | , Graph DBMSs), three tools [ 9,10,15] that aim for detecting transactional anomalies, and four tools [ 13,32,54,55] without publicly available links or artifacts related to the bug reports. |
citation marker project authored |
4.1 Dataset Construction and Curation page 13 |
| M10 | This bug was found by DQP [ 5] and marked as critical by developers. |
technique |
4.4 Extended Applications and Discoveries page 17 |
| M11 | Recent work [ 13,54] highlights the benefits of mining historical bug patterns to inform automated testing tools. |
citation marker project authored |
4.4 Extended Applications and Discoveries page 18 |
| M12 | Key challenges include differences in supported statements, functions, operators, and type systems [ 67,68]. |
citation marker |
5 Discussion page 21 |
| M13 | , PQS [ 43], TLP [ 42], and NoREC [ 41], with recent work further specializing in optimizer-centric settings [ 3,5,22,48,49, 51,63]. |
technique |
6 Related Work page 21 |
| M14 | Transactional correctness relies on graph-based oracles and isolation checking [ 9,10,21], while performance issues are targeted via regression and cardinality-estimation testing [ 4,24,33]. |
citation marker project authored |
6 Related Work page 21 |
| M15 | [ 13] analyze built-in SQL function bugs, Wu et al. |
citation marker project authored |
6 Related Work page 21 |