BDD Framework Lexical Parser for Graph Query Verification
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Solution Overview
Problem
The existing Behavior Driven Development (BDD) test frameworks cannot recognize complex Gherkin texts and fail to verify compatibility of graph query languages with the graph query language standard, limiting their functionality and effectiveness in graph database development.
Innovation Solution
A method and system are introduced that enhance the BDD test framework by adding a lexical parser and preset lexical rules, allowing migration of Gherkin texts from a graph query language standard to a graph query language, parsing, executing, and comparing results to ensure compatibility with the graph query language standard, thereby enabling recognition of complex Gherkin texts and verification of graph query language standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a BDD test framework uses simple Gherkin text recognition, then the framework is easy to implement, but it cannot recognize complex Gherkin texts and has single function
Solution Approach 1:
The patent segments the Gherkin text parsing process into multiple stages: lexical analysis (using a lexical parser to tokenize the text), syntax analysis (using a parser to construct parse trees), and semantic analysis (using a evaluator to execute the query). This segmentation allows the framework to handle complex Gherkin texts systematically while maintaining manageable complexity through modular processing stages.
Solution Approach 2:
The patent introduces a lexical parser as an intermediary component between the Gherkin text input and the query execution system. This lexical parser translates the human-readable Gherkin syntax into an intermediate representation that can be processed by the graph query language evaluator, enabling the framework to recognize and process complex Gherkin texts without directly implementing all parsing logic.
2Adaptability or versatility
If a BDD test framework has single function, then the framework is simple, but it cannot verify compatibility of graph query language with standard
Solution Approach 1:
The patent implements a universal testing framework that can perform multiple functions: executing graph query language queries, parsing Gherkin test specifications, comparing query results against expected outcomes, and verifying compatibility with graph query language standards. This multi-functional design allows a single framework to handle both basic testing and standard compliance verification without requiring separate specialized tools.
Solution Approach 2:
The patent incorporates a feedback mechanism where the framework compares the actual query results against the expected results defined in the Gherkin test specifications. This feedback loop enables the framework to verify whether the graph query language implementation conforms to the standard by detecting discrepancies between expected and actual behavior, providing automated compatibility verification.
3Reliability
If graph query language is not standardized, then implementation is flexible, but compatibility issues arise and development is hindered
Solution Approach 1:
The patent implements a self-service verification mechanism where the framework automatically compares graph query language implementations against the standard using Gherkin test specifications. Developers can write test cases in the standard Gherkin format, and the framework automatically executes the queries, compares results, and reports compliance status. This self-service approach eliminates the need for manual compatibility checking and accelerates development by providing immediate feedback on standard compliance.
Data Source
AI summary
Disclosed are a method and system for verifying a resulting behavior of a graph query language. A lexical parser and a preset lexical rule are added to a BDD test framework, to construct an improved BDD test framework; a first Gherkin text in a graph query language standard is migrated to be a second Gherkin text in a graph query language; a first parsing result of the second Gherkin text is acquired from the improved BDD test framework; a first return result from a graph database is acquired, a comparison result between the first parsing result and the first return result is acquired, wherein the graph query language meets the graph query language standard if the first parsing result and the first return result are the same. The present disclosure solves the problem that the BDD test framework cannot recognize complex Gherkin texts, thereby facilitating development of graph databases.