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Chenglin Tian

dblp:380/0273 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0002-4343-2586ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data models and query languages · 77% Database system architecture and tuning · 23%
Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages › SQL
SQL semantics
0.812024
Semantic Conformance Testing of Relational DBMS · Proc. VLDB Endow. 2024
Software testing › specification-based testing
conformance testing
0.812024
Semantic Conformance Testing of Relational DBMS · Proc. VLDB Endow. 2024
Database system architecture and tuning
relational database system
0.212024
Semantic Conformance Testing of Relational DBMS · Proc. VLDB Endow. 2024

Methods — techniques the papers use, named apart from their topics

prolog · 1.5differential testing · 1.5coverage-guided query generation · 1.5
YearPublicationVenuePosition
2026 DBRooter: An Efficient Causal Root Cause Analysis Framework for Distributed Databases
Qingfeng Xiang, Yingxia Shao, Chenglin Tian, Quanqing Xu, Qiyao Luo
DASFAA (2)3
2026 LLMSQLMUTATOR: LLM-Powered Test Case Generation for Database Using Bug Reports
Chenglin Tian, Yingxia Shao
ICDE1
2024 Semantic Conformance Testing of Relational DBMS
abstract
Relational DBMS implementations are expected to adhere to SQL standards. However, there are currently no tools available that can automatically verify this conformance. The main reasons are twofold. First, the SQL standard specification, documented in natural language, tends to be ambiguous and is not directly executable. Second, it is difficult to generate test queries that thoroughly cover all aspects, e.g., keywords and parameters, defined in the SQL specification. In this work, we introduce the first method for semantic conformance testing of RDBMSs. Our contributions are threefold. Firstly, we formally define the denotational semantics of SQL and implement them in Prolog, creating an executable reference RDBMS for differential testing against existing RDBMSs. Secondly, we propose three coverage criteria based on these formal semantics, along with a coverage-guided query generation algorithm that effectively generates queries achieving high semantic coverage. Lastly, we apply our approach to six widely-used and thoroughly tested RDBMSs, e.g., MySQL, PostgreSQL and OceanBase, uncovering 19 bugs and 13 inconsistencies, all of which are confirmed by RDBMS developers.
Shuang Liu 0007, Chenglin Tian, Jun Sun 0001, Wei Lu 0015, Yinxing Xue, Junjie Wang 0007, Xiaoyong Du 0001
Proc. VLDB Endow.2