ML Script Interruption Handling for Test Resume Automation
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Solution Overview
Problem
In continuous testing environments, test automation scripts often encounter interruptions that require restarting from the beginning, leading to redundancy and delays due to the inability to resume testing from the interruption point.
Innovation Solution
A machine learning-based script interruption handling platform that monitors test automation scripts in real-time, identifies interruptions, applies corrective actions, and resumes testing from the interruption point using a trained machine learning model and intelligence database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire test automation script is re-executed from the beginning after an interruption, then the testing can be completed, but time is wasted and resources are depleted due to re-executing already successfully tested portions
Solution Approach 1:
The system performs preliminary actions by monitoring test execution in real-time and identifying interruptions before they cause complete script failure. It captures the state at the interruption point and prepares resumption capabilities in advance, so when an interruption occurs, the system can immediately resume from that point rather than restarting from the beginning.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring test script execution status and detecting interruptions. When an interruption is detected, the system provides feedback about the interruption type and location, then uses this information to determine the appropriate corrective action and resume point, creating a closed-loop control system that adapts to interruptions.
2Reliability
If manual intervention is required to handle script interruptions, then corrective actions can be taken, but productivity decreases due to delays and resource requirements
Solution Approach 1:
The system implements self-service by automatically detecting interruptions, classifying them using machine learning models, determining appropriate corrective actions, and resuming test execution without requiring manual intervention. The system serves itself by maintaining its own execution state and making autonomous decisions about how to handle various interruption scenarios.
Solution Approach 2:
The machine learning model acts as an intermediary between the interruption detection system and the corrective action execution system. It receives raw interruption data, processes it to identify the type and cause of interruption, and outputs the appropriate corrective action, thereby automating what would otherwise require manual human judgment and intervention.
3Extent of automation
If a machine learning model is trained on historical interruption data to identify corrective actions, then automation and accuracy improve, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it classifies interruption types, identifies potential causes, and determines appropriate corrective actions. This multi-functionality reduces the need for separate specialized systems for each task, thereby managing complexity while achieving high automation. The same model infrastructure is used for multiple purposes in the interruption handling pipeline.
Data Source
AI summary
Aspects related to machine learning-based script interruption handling are provided. A computing platform may train a machine learning model to identify, for a test script interruption, a corrective action to resolve the interruption. The platform may receive information and details corresponding to an interruption associated with a test automation script. The platform may identify, by executing a machine learning model, a cause of the interruption and a predicted corrective to resolve the interruption. The platform may cause, based on identifying the predicted corrective action, initiation of the corrective action. The platform may update, based on the corrective action, the machine learning model. The platform may also resume the test automation script from the point of interruption.