Problem
Research questions and friction points this paper is trying to address.
DBMS bugs
bug repository
test case generation
automated testing
bug report heterogeneity
BugForge: Constructing and Utilizing DBMS Bug Repository to Enhance DBMS Testing
🤖 AI Summary
This work addresses the challenges posed by heterogeneous formats and incomplete information in database management system (DBMS) bug reports, which hinder their effective conversion into high-quality test cases. To overcome this, the authors propose BugForge, a novel framework that leverages syntax-aware processing and input-adaptive extraction to construct the first standardized DBMS bug repository from raw proof-of-concept (PoC) artifacts. Integrated with a semantics-guided test case adaptation mechanism, BugForge automatically generates reproducible, high-value test cases. The approach supports fuzz testing, regression testing, and cross-DBMS bug discovery. Evaluated on PostgreSQL, MySQL, MariaDB, and MonetDB using 37,632 historical bug reports, BugForge successfully uncovered 35 new bugs—22 of which have been confirmed—demonstrating significant improvements in both defect detection efficiency and coverage.
📝 Abstract
DBMSs are complex systems prone to bugs that may lead to system failures or compromise data integrity. Establishing unified DBMS bug repositories is crucial for systematically organizing bug-related data, enabling code improvement, and supporting automated testing. In particular, bug reports often contain valuable test inputs and bug-triggering clues that help explore rare execution paths and expose critical buggy behavior, thereby guiding automated DBMS testing. However, the heterogeneity of bug reports, along with their incomplete or inaccurate content, makes it challenging to build unified repositories and convert them into high-quality test cases.
In this paper, we propose BugForge, a framework that constructs standardized DBMS bug repositories and leverages them to generate high-quality test cases to enhance DBMS testing. Specifically, BugForge progressively collects bug reports, then employs syntax-aware processing and input-adaptive raw PoC extraction to construct a DBMS bug repository. The repository stores structured bug-related data, including bug metadata and raw PoCs that entail potential bug-triggering semantics. These data are further refined into high-quality test cases through semantic-guided adaptation, thereby enabling enhanced DBMS testing methods, including DBMS fuzzing, regression testing, and cross-DBMS bug discovery. We implemented BugForge for PostgreSQL, MySQL, MariaDB, and MonetDB, totally integrated 37,632 bug reports spanning up to 28 years. Based on the repository, BugForge uncovered 35 previously unknown bugs with 22 confirmed by developers, demonstrating the value of constructing and utilizing bug repositories for DBMS testing.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bug repository
DBMS testing
PoC extraction
Semantic-guided adaptation
Automated test generation
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