AI Test Script Generation from Natural Language Using Evolving Models
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Solution Overview
Problem
Existing methods struggle to efficiently convert natural language test cases into automated test scripts for applications, particularly in the context of time-evolving models, due to the lack of effective mechanisms for updating and integrating new knowledge about application states and actions.
Innovation Solution
A method involving a test script generator that utilizes a blueprint of the application, including a time-evolving model with a state machine, to automatically convert test cases into test scripts. This process involves segmenting test cases, determining application states and actions, and updating the model with new knowledge, using artificial intelligence for semantic similarity and large language models to handle images and text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static model of the application is used for test script generation, then the initial test scripts can be created, but the test scripts become outdated when the application evolves, requiring manual updates and reducing reliability
Solution Approach 1:
The patent implements a time-evolving model that dynamically updates application state representations as the application changes. The model captures temporal relationships between application states and automatically adapts when new states or transitions are detected, eliminating the need for static, manually-updated models and maintaining test script reliability over time.
Solution Approach 2:
The system incorporates feedback mechanisms where test execution results and application state observations are fed back into the time-evolving model. This feedback loop allows the model to learn from actual application behavior and automatically update its understanding of application states and transitions, ensuring test scripts remain synchronized with the evolving application without manual intervention.
2Adaptability or versatility
If traditional automated test script generation methods are used, then test scripts can be created, but they fail to adapt to application changes and require extensive manual maintenance
Solution Approach 1:
The patent performs preliminary actions by building a time-evolving model that proactively captures and stores temporal relationships between application states before testing begins. This pre-established model structure enables automatic adaptation to application changes during testing, eliminating the need for complex manual maintenance while improving adaptability.
Solution Approach 2:
The time-evolving model serves as an intermediary layer between the application under test and the test script generation system. This intermediary automatically processes application state changes and translates them into updated test scripts, reducing the complexity of direct maintenance while enhancing adaptability to application evolution.
3Measurement precision
If detailed application state tracking is implemented, then test accuracy improves, but the complexity of model construction and maintenance increases
Solution Approach 1:
The patent segments the application state space into discrete, manageable states and transitions within the time-evolving model. By dividing the complex application behavior into distinct state segments with clear transition criteria, the system achieves precise state detection while keeping the model construction and maintenance complexity tractable through modular organization.
Data Source
AI summary
A natural language test case for an application is automatically converted into a test script by a test script generator. During the generation of the test script, the test script generator may access a blueprint of the application. The blueprint may include a time-evolving model of the application, which may include a set of translations and a state machine model of the application. The state machine model may include a state space and a set of state transitions. During the test script generation process, the blueprint may be continually updated to include new knowledge of the application. The test script generator may communicate with an artificial intelligence (AI) engine in order to determine the semantic similarity between two phrases, convert images into textual description, and perform tasks requiring the use of a large language model (LLM).