Database connectors are widely used in many applications to facilitate flexible and convenient database interactions. Potential bugs in database connectors can lead to various abnormal behaviors within applications, such as returning incorrect results or experiencing unexpected connection interruption. However, existing DBMS fuzzing works cannot be directly applied to testing database connectors as they mainly focus on SQL generation and use a small subset of connector interfaces. Automated test case generation also struggles to generate effective test cases that explore intricate interactions of database connectors due to a lack of domain knowledge. The main challenge in testing database connectors is generating semantically correct test cases that can trigger various connector state transitions. To address that, we propose CONI, a framework designed for detecting logic bugs of database connectors with state-aware test case generation. First, we define the database connector state model by analyzing the corresponding standard specification. Building upon this model, Coni generates interface call sequences within test cases to encompass various state transitions. After that, Coni generates suitable parameter values based on the parameter information and contextual information collected during runtime. Then the test cases are executed on a target and a reference database connector. Inconsistent results indicate potential bugs. We evaluated CONI on 5 widely-used JDBC database connectors, namely MySQL Connector/J, MariaDB Connector/J, AWS JDBC Driver for MySQL, PGJDBC, and PG JDBC NG. In total, Coni reported 44 previously unknown bugs, of which 34 have been confirmed.

Data durability is a fundamental requirement in DBMSs, ensuring that committed data remains intact despite unexpected faults such as power failures. Despite its critical importance, implementations of durability and recovery mechanisms continue to exhibit flaws, leading to severe issues(e.g., data loss, data inconsistency), which we refer to as Data Durability Bugs (DDBs). However, there is a limited understanding of the characteristics and root causes of DDBs. Furthermore, existing testing methods(e.g., Mallory) are often inadequate for detecting DDBs, particularly those that cause data loss or data inconsistency following DBMS failures.

Database Management Systems (DBMS) perform various data operations such as arithmetic calculations and string manipulations when executing SQL queries. These operations are complex due to the wide range of data types and the intricate interactions between different data. Consequently, errors in implementing these data operations can lead to logic bugs, potentially causing issues such as implicit type coercion, overflow, and precision loss. Existing logic bug detection methods primarily focus on issues introduced during query optimization by adapting query-level strategies. However, these methods have limitations when it comes to detecting logic bugs caused by implementation errors in data types and operations. To address this, we propose equivalent data construction (EDC), a novel approach to detect logic bugs in data operation implementations within DBMSs. The core insight is that for data operation expressions in SQL queries, substituting them with precomputed result values should yield identical query outcomes. EDC mainly involves the following steps: first, construct equivalent data for an operation by calculating and storing the results in a derived equivalent table; then, transform the query by replacing the operation expressions with the precomputed results from the equivalent table. Any inconsistencies between the results of the base and transformed queries indicate potential logic bugs. We implemented EDC and evaluated it on six well-tested and widely-used DBMSs(e.g.,MySQL, MariaDB). Our evaluation revealed 52 previously unknown bugs, of which 38 have been confirmed by developers. Developers took these findings seriously. For example, MariaDB developers described our findings as counterintuitive, helping them uncover more issues related to data operations.