Random Query Framework for DBMS Feature Failure Testing
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Solution Overview
Problem
Existing database management system (DBMS) testing frameworks are limited by preconfigured test cases and fail to adequately identify points of failure and performance degradation due to the inability to generate a wide range of queries and test various database feature combinations.
Innovation Solution
An extensible DBMS testing framework that generates random queries based on a query grammar, enabling and disabling database features randomly to identify performance issues and failures through performance data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If preconfigured test cases are used, then testing framework complexity is reduced, but testing coverage and ability to identify points of failure deteriorates
Solution Approach 1:
The testing framework transitions from static preconfigured test cases to dynamic random query generation. The query generator creates tests on-the-fly based on database schemas and grammars, enabling adaptive coverage of the input space while maintaining framework simplicity through modular component design.
Solution Approach 2:
The framework changes test parameters dynamically by randomly selecting queries, database features to enable/disable, and test conditions. This parameter variation approach expands testing coverage without increasing framework complexity, as the same simple framework handles diverse test scenarios through random parameter generation.
2Reliability
If random queries are generated to cover broader input space, then testing coverage improves, but query generation and execution time increases
Solution Approach 1:
The framework executes queries with randomly enabled/disabled database features, performing partial testing of feature combinations rather than exhaustive testing. This partial action approach achieves sufficient testing coverage and identifies points of failure without the time cost of complete exhaustive testing of all possible feature combinations.
Solution Approach 2:
The query generator creates test queries in advance based on database schemas and grammars before execution. This preliminary generation of test cases allows the system to prepare test scenarios ahead of time, reducing execution time while maintaining broad coverage through random query generation.
3Reliability
If database features are randomly enabled or disabled, then ability to identify interactions and failures improves, but testing configuration complexity increases
Solution Approach 1:
The testing framework performs self-service by automatically generating random queries and feature combinations without requiring manual configuration. The query generator and feature enablement mechanisms operate autonomously based on database schemas, eliminating the need for complex manual test configuration while accurately identifying feature interactions and failures.
Solution Approach 2:
The framework segments the testing process into independent components: query generator, feature enablement controller, query executor, and result analyzer. This segmentation allows each component to handle specific tasks independently, reducing overall configuration complexity while maintaining the ability to identify failures through random feature combinations.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage media for testing features of a database management system (DBMS). A DBMS testing framework generates new random test cases for testing database features on the system. The framework receives a query grammar specifying the structure of queries to generate and generates the queries randomly. The framework executes the queries with database features randomly enabled or disabled and generates performance data from the results of executing those queries. The framework identifies points of failure in the performance data, corresponding to instances in which queries executed with certain combinations of database features result in incorrect output, or degraded performance relative to executing the queries without the database features enabled. The testing framework divides the database preparation, query generation, and query execution parts of a test pipeline into separate components, which can be modified separately or left to proceed in a default operating mode.