Randomized Differential Testing of RDF Stores

Yang, Rui; Zheng, Yingying; Tang, Lei; Dou, Wensheng; Wang, Wei; Wei, Jun

doi:10.1109/icse-companion58688.2023.00041

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The core idea behind differential testing is by feeding the same input to many functionally identical systems and comparing their outputs to detect bugs. There are many existing works applying differential testing on DBMSs [8], [30]- [32], [45]- [49]. RAGS [8] executes the same SELECT query on different DBMSs and observes discrepancies in their query results.…”

Section: Related Workmentioning

confidence: 99%

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Testing Database Systems via Differential Query Execution

Song

Dou

Cui

et al. 2023

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

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Database Management Systems (DBMSs) provide efficient data retrieval and manipulation for many applications through Structured Query Language (SQL). Incorrect implementations of DBMSs can result in logic bugs, which cause SELECT queries to fetch incorrect results, or UPDATE and DELETE queries to generate incorrect database states. Existing approaches mainly focus on detecting logic bugs in SELECT queries. However, logic bugs in UPDATE and DELETE queries have not been tackled.In this paper, we propose a novel and general approach, which we have termed Differential Query Execution (DQE), to detect logic bugs in SELECT, UPDATE and DELETE queries of DBMSs. The core idea of DQE is that different SQL queries with the same predicate usually access the same rows in a database. For example, a row updated by an UPDATE query with a predicate φ should also be fetched by a SELECT query with the same predicate φ. If not, a logic bug is revealed in the target DBMS. To evaluate the effectiveness and generality of DQE, we apply DQE on five production-level DBMSs, i.e., MySQL, MariaDB, TiDB, CockroachDB and SQLite. In total, we have detected 50 unique bugs in these DBMSs, 41 of which have been confirmed, and 11 have been fixed. We expect that the simplicity and generality of DQE can greatly improve the reliability of DBMSs.

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Section: Related Workmentioning

confidence: 99%

“…DT2 [46] feeds a group of transactions into multiple DBMSs to detect transaction bugs. Grand [45] and RD2 [49] apply differential testing on graph database systems. APOLLO [30] feeds the same SELECT query into two different versions of the same DBMS to detect performance bugs.…”

Section: Related Workmentioning

confidence: 99%