AI Test Script Migration Across Incompatible Automation Frameworks
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Solution Overview
Problem
Existing test automation frameworks are often incompatible, requiring significant manual recoding efforts when organizations want to switch from one framework to another, hindering the adoption of better-suited frameworks.
Innovation Solution
An AI model is used to parse and convert automation scripts from a first framework to a second framework by generating prompts and converting subsets of code, with iterative refinement to ensure compatibility and adherence to framework and organizational standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual recoding is performed to migrate automation scripts between incompatible frameworks, then compatibility with the new framework is achieved, but significant time and effort are lost
Solution Approach 1:
The patent replaces manual mechanical recoding work with an AI-based automated conversion system. The processor executes conversion logic that automatically transforms automation scripts from one framework to another, substituting human manual effort with computational processing. This resolves the contradiction by maintaining framework compatibility through automated conversion while eliminating the time loss associated with manual recoding.
Solution Approach 2:
The patent introduces an AI-based conversion system as an intermediary between incompatible frameworks. This intermediary automatically translates scripts from the source framework to the target framework, enabling compatibility without direct manual intervention. The intermediary handles the complex transformation process, resolving the contradiction by achieving framework compatibility while minimizing migration time through automated mediation.
2Ease of operation
If automation scripts are manually modified to integrate with a new framework, then the scripts become usable in the new framework, but significant manual coding time is spent
Solution Approach 1:
The patent replaces manual script modification work with automated AI-based conversion. The system automatically analyzes the source script structure, identifies framework-specific elements, and transforms them into the target framework's syntax and conventions. This substitutes manual coding time with automated processing while ensuring the converted scripts are fully usable in the new framework.
Solution Approach 2:
The patent performs preliminary analysis of the source automation script to understand its structure, dependencies, and framework-specific elements before conversion. This preliminary action enables the AI system to plan and execute the transformation efficiently, reducing the overall migration time while ensuring the resulting scripts are immediately usable in the target framework without requiring additional manual adjustments.
3Loss of time
If existing automation scripts are used without modification, then development time is preserved, but the scripts cannot be executed by a different framework
Solution Approach 1:
The patent replaces manual script rewriting with automated conversion processing. The AI system takes the existing automation scripts as input, automatically analyzes their structure and logic, and generates equivalent scripts in the target framework's syntax. This preserves the original development time investment by maintaining the script logic while automatically adapting the syntax, thus resolving the contradiction between preserving development time and achieving framework compatibility.
Solution Approach 2:
The patent creates a converted copy of the original automation script that is compatible with the target framework. The AI system replicates the script's functionality and logic while transforming the syntax and structure to match the new framework's requirements. This copying approach preserves the original development investment while enabling cross-framework execution, resolving the contradiction by maintaining both development time efficiency and framework adaptability.
Data Source
AI summary
An example operation may include one or more of storing an artificial intelligence (AI) model in a memory, receiving an automation script of a software test that is associated with a first automation framework, parsing the automation script to identify a subset of code for the software test, generating a prompt which includes instructions for converting the automation script into a form associated with a second automation framework, converting the subset of code into a converted subset of code that is associated with the second automation framework based on execution of the AI model on the prompt and the subset of code, creating a converted automation script based on the converted subset of code and displaying the converted automation script via a graphical user interface (GUI), and associating the converted automation script with the second automation framework.