Intelligent Mobile Device Selection for App Testing
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Solution Overview
Problem
The increasing variety of mobile devices, including wearable and IoT devices, makes it impractical and costly to exhaustively test new mobile applications on all available devices, leading to potential defects and user complaints about application functionality.
Innovation Solution
An intelligent device selection facility that uses automated analysis of user reviews to identify risk scores for new mobile applications, recommending a subset of devices for testing based on features and user feedback, thereby prioritizing devices where similar applications have experienced issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive testing on all available mobile devices is performed, then application reliability is improved, but testing cost and time increase significantly
Solution Approach 1:
The patent segments the testing process by dividing mobile devices into different priority groups based on risk scores. Instead of treating all devices equally, the system categorizes devices into high-priority, medium-priority, and low-priority segments, allowing targeted testing resources to be allocated efficiently while maintaining application reliability.
Solution Approach 2:
The patent performs preliminary analysis of user reviews and device characteristics before actual testing begins. By pre-calculating risk scores based on historical data, device compatibility patterns, and user feedback, the system prepares a prioritized testing roadmap in advance, reducing the complexity of the actual testing execution phase.
2Measurement precision
If testing is performed on a large number of devices, then defect detection capability is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by assigning different testing intensities to different device groups. High-risk devices receive intensive testing with multiple test cases and thorough verification, while low-risk devices receive minimal or no testing. This localized approach to testing quality ensures defect detection capability is concentrated where it is most needed, optimizing resource consumption.
Solution Approach 2:
The patent uses virtual device profiles and simulated testing environments to replicate the behavior of multiple physical devices. By creating virtual copies of device characteristics and using automated testing frameworks that can simulate various device conditions, the system achieves broad defect detection coverage without requiring proportional physical device resources.
3Adaptability or versatility
If all mobile device variations are tested, then compatibility assurance is improved, but testing time extends indefinitely
Solution Approach 1:
The patent implements partial action by testing only the necessary subset of devices required to achieve adequate compatibility assurance. Rather than exhaustively testing every device variation, the system identifies and tests a representative sample of high-risk devices that, when validated, provide sufficient confidence for broader compatibility. This approach achieves practical compatibility assurance within reasonable time constraints.
Solution Approach 2:
The patent makes the testing scope dynamic by continuously updating device priority rankings based on new user reviews, emerging compatibility issues, and changing application requirements. The testing plan adapts in real-time, focusing resources on currently high-risk device combinations while reducing or eliminating testing on low-risk devices, thereby maintaining compatibility assurance efficiently over time.
Data Source
AI summary
A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.