Automated BDD Test Model Verification via Path Analysis
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Solution Overview
Problem
In large and complex software development projects, Behavior Driven Development (BDD) approaches face challenges in managing numerous scenarios and ensuring completeness and consistency of test sets, as traditional manual methods are inefficient and only applicable to small projects.
Innovation Solution
A computer-implemented method for automated verification of BDD test scenarios using model-based path analysis, natural language processing, and machine learning to generate a model path tree, filter irrelevant steps, identify missing alternative test steps, and add them to the test model, resulting in improved test coverage and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual BDD test scenario management is used, then ease of operation is maintained for small projects, but productivity deteriorates in large and complex projects
Solution Approach 1:
The patent replaces manual mechanical operations with an automated computing system that performs model-based path analysis, natural language processing, and machine learning to automatically verify BDD test scenarios, identify missing test steps, and generate recommendations, thereby maintaining ease of operation while dramatically improving productivity for large-scale projects
Solution Approach 2:
The system enables self-service by automatically analyzing test models, identifying completeness and consistency issues, and generating remediation recommendations without requiring manual intervention, allowing the test suite to self-verify and self-improve while maintaining operational simplicity
2Device complexity
If manual verification methods are used, then device complexity is low, but measurement precision deteriorates in ensuring completeness and consistency
Solution Approach 1:
The patent replaces manual verification with an automated computing system that uses model-based path analysis, natural language processing, and machine learning to precisely measure and verify the completeness and consistency of BDD test scenarios, achieving high measurement precision through algorithmic analysis rather than human inspection
Solution Approach 2:
The system introduces an intermediary computing unit that acts as a mediator between the test model and the verification process, using natural language processing and machine learning algorithms to bridge the gap between manual verification capabilities and the need for precise automated completeness and consistency checking
3Productivity
If automated verification using model-based path analysis is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the verification process into distinct modular components: model-based path analysis module, natural language processing module, machine learning module, and recommendation generation module. Each module handles a specific aspect of verification, improving productivity through automated parallel processing while managing complexity through clear separation of concerns and independent module design
Solution Approach 2:
The computing unit is designed as a universal platform that performs multiple functions including path analysis, natural language processing, machine learning inference, and recommendation generation within a single integrated system, achieving high productivity through multi-functionality while managing complexity through unified architecture rather than separate systems
4Ease of operation
If traditional BDD approaches are used, then ease of operation is maintained, but loss of information occurs in managing numerous scenarios
Solution Approach 1:
The patent replaces manual tracking and management of BDD test scenarios with an automated computing system that uses model-based path analysis and natural language processing to systematically analyze and verify each scenario, preventing information loss through automated documentation and verification of test coverage, completeness, and consistency across numerous scenarios
Solution Approach 2:
The system implements feedback mechanisms by automatically analyzing test models, identifying missing or inconsistent test steps, and generating actionable recommendations for improvement. This closed-loop feedback process ensures that information about test scenario completeness and consistency is continuously monitored and maintained, preventing information loss while keeping the system easy to operate through automated guidance
Data Source
AI summary
A—method for automated verification of a test model for BDD test scenarios including receiving the test model to be verified; generating a model path tree using a model-based path analysis based on the test model; filtering any test step of the plurality of test steps of the paths in the model path tree; d. identifying any test step with a missing alternative test step in another alternative path of the plurality of paths in the model path tree using machine learning based on the model path tree; adding at least one missing alternative test step to the test model after identification of at least one test step, resulting in at least one alternative path; and providing the identified test step, the identified at least one missing alternative test step, the at least one alternative path and/or the at least one alternative BDD test scenario as output data.