Generative AI Test Flow Adaptation for Multi-Channel Applications
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Solution Overview
Problem
Current application testing methods struggle to cover all possible scenarios across multiple channels due to the need for manual script adjustments, which is time-consuming and inefficient.
Innovation Solution
Utilizing generative AI to automatically adapt test automation flows for different application versions across various channels, leveraging historical testing data to generate and validate adapted test automation scripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual script adjustments are made for each channel, then testing coverage can be customized per channel, but the time and effort required increases significantly
Solution Approach 1:
The system creates channel-specific test scripts by copying and adapting a master test script. The generative AI model generates customized scripts for each channel by modifying the master script template, thereby maintaining consistent testing coverage across channels while eliminating manual script adjustment efforts.
Solution Approach 2:
The system enables self-service automation where the generative AI model automatically generates and adapts test scripts for multiple channels without human intervention. The model takes the master script and channel-specific parameters as input, and autonomously produces customized test scripts, freeing testers from manual script adjustment tasks.
2Ease of manufacture
If a single master test script is used for all channels, then script maintenance is simplified, but channel-specific testing scenarios cannot be adequately covered
Solution Approach 1:
The master test script is designed as a universal template that can serve all channels. It contains common testing logic and structure that applies across channels, while allowing for channel-specific customizations through parameters and configurations. This universal template approach simplifies maintenance while maintaining adaptability.
Solution Approach 2:
The system applies local quality by allowing channel-specific customizations in specific sections of the test script while maintaining the universal structure. The generative AI model identifies which parts of the master script need channel-specific modifications and applies localized changes, preserving the benefits of a single master script while achieving channel-specific coverage.
3Reliability
If test automation scripts are highly customized for each channel, then channel-specific scenarios are well-covered, but the complexity of managing multiple scripts increases
Solution Approach 1:
The test script structure is segmented into modular components: a universal master template and channel-specific customization sections. The generative AI model generates scripts by combining the master template with channel-specific segments, allowing for comprehensive scenario coverage while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The system adds a new dimension to script management by introducing channel identifiers as a parameter dimension. Instead of managing completely separate scripts for each channel, the system manages a single master script with channel-specific parameters, transforming the management problem from multiple independent objects to one object with variable dimensions.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for multi-channel application testing using generative AI. A test automation flow generated for an application is accessed. Generative artificial intelligence (AI) is used to automatically adapt the test automation flow to a plurality of versions of the application corresponding to different channels. The plurality of versions of the application are tested using the adapted test automation flows.