Automated Script Reclassification for Test Execution Failure Recovery
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Solution Overview
Problem
Automated application testing scripts often fail due to misclassification of user actions as irrelevant, leading to execution failures, especially with complex user interactions like mouse movements and dropdown menus, where essential actions are omitted, causing scripts to fail without human intervention.
Innovation Solution
An automated system reclassifies recorded user actions by iteratively re-evaluating previously classified irrelevant actions and revising the script to ensure successful execution, starting from the failed action and attempting previous omitted actions until the script executes correctly, with user confirmation for final script approval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the script generation application automatically selects recorded actions based on initial classification criteria, then the script generation process is efficient and automated, but essential actions may be misclassified as irrelevant and omitted, causing script execution failures
Solution Approach 1:
The system implements feedback by detecting script execution failures and automatically reclassifying previously omitted recorded actions. When a script execution fails, the system identifies the failure point and re-evaluates the classification of actions that were previously marked as irrelevant, using this feedback loop to improve script reliability while maintaining automation.
Solution Approach 2:
The system performs preliminary classification of recorded actions before script generation, but also prepares for reclassification by maintaining access to the full set of recorded actions. This preliminary action enables quick reclassification when failures occur, resolving the contradiction between automation efficiency and execution reliability.
2Reliability
If the system includes all recorded actions in the script, then script execution reliability improves, but the device complexity and processing overhead increase
Solution Approach 1:
The system applies partial action by initially including only classified relevant actions in the script. When execution fails, it selectively adds back specific previously omitted actions that are likely to be relevant, rather than including all recorded actions. This approach improves reliability while minimizing the increase in script complexity.
Solution Approach 2:
The script composition is made dynamic, allowing the system to adjust the included actions based on execution feedback. The script structure evolves from a static initial classification to a dynamic composition that adapts by selectively incorporating previously omitted actions when needed, balancing reliability and complexity.
3Measurement precision
If manual review and adjustment of recorded actions is performed, then classification accuracy improves, but the time and human resources required increase significantly
Solution Approach 1:
The system performs self-service by automatically detecting script execution failures and initiating the reclassification process without human intervention. The system autonomously identifies failed actions, re-evaluates previously omitted recorded actions, and generates revised scripts, eliminating the need for manual review while maintaining high classification accuracy.
Solution Approach 2:
The automated feedback mechanism replaces manual review by using script execution results to trigger intelligent reclassification. This feedback-driven approach achieves high classification accuracy automatically, resolving the contradiction between precision and time consumption.
4Productivity
If the system reclassifies and revises scripts automatically upon failure, then productivity and automation extent improve, but the complexity of the script generation system increases
Solution Approach 1:
The system extracts the reclassification logic as a separate, modular component that activates only when script execution fails. This extraction isolates the complexity into a dedicated failure-handling module, improving productivity through automation while managing system complexity through modular design.
Solution Approach 2:
The script generation and revision process is segmented into distinct phases: initial classification, script generation, execution monitoring, failure detection, and reclassification. This segmentation allows the system to automate the overall process while managing complexity through clear separation of concerns.
Data Source
AI summary
A method for automatic revision of an automation script includes obtaining a sequence of at least one classified recorded action and an automation script, the automation script including a sub-sequence of the sequence of classified recorded actions, wherein each action is included in the automation script in accordance with the classification of that action. At least a portion of the automation script is executed. Upon failure of an action of the portion of the automation script to execute, an action of the sequence of classified recorded actions is reclassified, it is verified if the action that failed to execute executes successfully after the reclassifying, and the automation script is revised. Relating computer program product and data processing system are also disclosed.