Enhanced Differential Testing in Emerging Database Systems

2025-01-02

AI Summary

Emerging time-series and streaming SQL databases suffer from logic errors and internal crashes due to their low maturity. To address this, we propose the first semantics-aware differential testing framework for relational databases, grounded in cross-system semantic mapping. Our approach overcomes syntactic and semantic heterogeneity by identifying shared SQL clauses, reverse-engineering semantic extensions of new features, and generating portable test inputs across systems. It integrates constraint-driven test case generation with multi-database collaborative validation. We evaluate the framework on four mainstream emerging databases, uncovering 57 previously unreported defects—17 logic bugs and 40 internal errors. Of these, 50 have been fixed by vendors and 5 confirmed. This work significantly advances reliability verification for emerging database systems.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

In recent years, a plethora of database management systems have surfaced to meet the demands of various scenarios. Emerging database systems, such as time-series and streaming database systems, are tailored to specific use cases requiring enhanced functionality and performance. However, as they are typically less mature, there can be bugs that either cause incorrect results or errors impacting reliability. To tackle this, we propose enhanced differential testing to uncover various bugs in emerging SQL-like database systems. The challenge is how to deal with differences of these emerging databases. Our insight is that many emerging database systems are conceptually extensions of relational database systems, making it possible to reveal logic bugs leveraging existing relational, known-reliable database systems. However, due to inevitable syntax or semantics gaps, it remains challenging to scale differential testing to various emerging database systems. We enhance differential testing for emerging database systems with three steps: (i) identifying shared clauses; (ii) extending shared clauses via mapping new features back to existing clauses of relational database systems; and (iii) generating differential inputs using extended shared clauses. We implemented our approach in a tool called SQLxDiff and applied it to four popular emerging database systems. In total, we found 57 unknown bugs, of which 17 were logic bugs and 40 were internal errors. Overall, vendors fixed 50 bugs and confirmed 5. Our results demonstrate the practicality and effectiveness of SQLxDiff in detecting bugs in emerging database systems, which has the potential to improve the reliability of their applications.

Problem

• Database Systems
• Error Detection
• Quality Improvement

Innovation

• SQLxDiff
• Differential Testing
• Database Error Detection