Adaptive Mobile Device Testing via Learning Algorithm
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Solution Overview
Problem
The complexity of modern mobile devices and communication networks increases the difficulty of performing comprehensive automated testing, especially when verifying network connectivity and user interface changes.
Innovation Solution
An automated testing arrangement that uses a learning algorithm to generate device test automation configurations, allowing for adaptation based on previous tests and machine learning feedback, enabling efficient testing across various devices and network configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing of all device functions and network configurations is performed, then testing coverage and reliability are improved, but testing time and system complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple possible test instruction sequences based on device information and network configurations before actual testing begins. This allows the testing system to have ready-made test paths that can be quickly executed without real-time analysis during the actual testing process, thereby reducing testing time while maintaining comprehensive coverage.
Solution Approach 2:
The system dynamically adapts test instruction sequences based on actual device responses and test outcomes. The testing process is not static but adjusts in real-time, selecting and modifying test sequences based on device characteristics, network conditions, and previous test results, optimizing the balance between coverage and time consumption.
2Measurement precision
If manual testing is used to handle device interface changes and unexpected behaviors, then testing accuracy is improved, but productivity and automation level decrease
Solution Approach 1:
The system implements feedback mechanisms where test outcomes and device responses are continuously analyzed. When unexpected behaviors or interface changes are detected, the system learns from these feedback signals and automatically adjusts future test sequences, improving accuracy without requiring manual intervention while maintaining high productivity through automation.
Solution Approach 2:
The testing system performs self-service by automatically detecting and adapting to device interface changes without human intervention. The system monitors its own testing process, identifies anomalies, and modifies test sequences autonomously, thereby maintaining high testing accuracy while preserving automation and productivity.
3Productivity
If fixed test sequences are used for automated testing, then productivity is improved, but adaptability to device changes and network variations decreases
Solution Approach 1:
The system employs dynamic test sequences that can adapt to different device characteristics and network configurations. Rather than using completely fixed sequences, the system generates and modifies test sequences based on device information, making the testing process both efficient and adaptable to variations in devices and networks.
Solution Approach 2:
The system changes test parameters and sequence configurations based on device-specific attributes and network conditions. By adjusting test parameters dynamically according to the being-tested device characteristics, the system maintains high productivity while achieving necessary adaptability to different devices and network environments.
Data Source
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AI summary
An arrangement for automated testing of mobile devices comprising a learning arrangement for learning how to use test devices that do not match with an earlier already defined test case pattern. In the arrangement the learning arrangement generates instructions for performing a set of tasks. The tasks are then executed in the mobile device being tested. The mobile device provides feedback in form of error/success messages, screenshots, source code, return values and similar. Based on the feedback and earlier accumulated information the learning entity can generate a new set of instructions in order to execute the set of tasks successfully.