GraphQL Query Evaluation Using Data Mutation Equivalence Tests
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Solution Overview
Problem
Current methods for generating and evaluating GraphQL queries face challenges in accurately aligning with intended data requirements, particularly in complex or large schemas, leading to issues like over-fetching or under-fetching, redundant schema structures, cyclic dependencies, and inaccurate evaluation that results in false positives or false negatives.
Innovation Solution
A system that includes a generation component to create GraphQL queries, a comparison component to assess syntactic and structural equivalence, a data mutation component to identify false positives/negatives through data variations, and an evaluation component to determine sufficiency, leveraging artificial intelligence for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GraphQL schemas grow in size and complexity to meet diverse data requirements, then the system can handle more data types and relationships, but accurately structuring and validating queries becomes more challenging
Solution Approach 1:
The system employs automated query generation and evaluation components that self-assess query accuracy against natural language inputs and execute equivalence testing, enabling the system to autonomously handle complex schema validation without increasing manual intervention
Solution Approach 2:
The evaluation component provides feedback on query sufficiency by comparing generated queries against natural language requirements and executing equivalence tests, creating a closed-loop system that continuously improves query accuracy as schemas grow in complexity
2Measurement precision
If traditional evaluation methods focus on surface-level query structure, then evaluation is simpler and faster, but the system cannot reliably confirm whether queries align with intended data requirements
Solution Approach 1:
The system performs preliminary syntactic and structural equivalence comparisons before executing full data mutation tests, filtering out obviously equivalent or non-equivalent queries early to reduce the complexity of comprehensive evaluation
Solution Approach 2:
The evaluation process is divided into distinct segments: syntactic equivalence comparison, structural equivalence comparison, and execution equivalence testing with data mutations, allowing each aspect to be evaluated independently and systematically
3Manufacturing precision
If post-processing adjustments are applied to refine generated queries, then query accuracy may improve, but the process requires schema-specific tuning and is impractical across diverse data configurations
Solution Approach 1:
The evaluation component implements a universal evaluation framework that works across diverse GraphQL schemas by comparing query structure and execution results rather than applying schema-specific rules, making the system adaptable to various data configurations without requiring custom tuning
Data Source
AI summary
A system includes a processor that executes computer executable components stored in a memory. The computer executable components can comprise a generation component that generates a GraphQL query. The computer executable components can further comprise a comparison component that compares a predefined GraphQL query to the generated GraphQL query. The computer executable components can further comprise a data mutation component that creates data variations to execute on the predefined GraphQL query and the generated GraphQL query to identify false positives or false negatives of execution equivalence of the queries. The computer executable components can further comprise an evaluation component that compares results of the executed queries to determine sufficiency of the generated GraphQL query.