Mobile App Test Case Generation via Risk-Based Path Segmentation
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Solution Overview
Problem
Software testing for mobile applications faces challenges in identifying a manageable subset of test cases that provide adequate coverage for various risk situations and state combinations, given the vast number of potential test cases due to multiple context parameters and their values.
Innovation Solution
A systematic test methodology is employed that involves identifying a test environment, risk situations, and context parameters, creating standard and variant operations paths, and analyzing these paths to design a set of test cases that cover critical areas, using a risk-based approach to reduce the number of test cases to a manageable level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive test cases are created to cover all context parameter combinations, then test coverage is improved, but the number of test cases becomes unmanageably large
Solution Approach 1:
The patent segments the test case space by dividing context parameters into independent groups (e.g., device context, network context, application context) and generating test cases through combinatorial selection rather than exhaustive enumeration. This segmentation allows comprehensive coverage of critical interactions while reducing the total test case count to manageable levels.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the selection criteria for context parameters based on risk assessment and business priorities. Instead of fixed comprehensive testing, the system adapts which parameters to test and at what depth, transforming the static test case generation into a dynamic process that balances coverage with feasibility.
2Reliability
If all context parameter combinations are tested, then testing completeness is improved, but testing complexity increases
Solution Approach 1:
The patent extracts and isolates the most critical context parameter combinations based on risk analysis and business rules. By taking out only the essential test cases that address high-risk scenarios and critical functionality, the system achieves testing completeness for what matters most while significantly reducing overall testing complexity.
Solution Approach 2:
The patent applies partial action by testing only the necessary subset of context parameter combinations rather than all possible combinations. The system determines the minimum sufficient set of test cases that provides adequate coverage for critical areas, avoiding the excessive complexity of exhaustive testing while maintaining acceptable testing completeness.
3Manufacturing precision
If extensive test case analysis is performed, then test quality is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing risk assessment frameworks, business rules, and context parameter prioritization criteria before test case generation. This preliminary structuring enables rapid, automated selection of high-quality test cases without requiring extensive manual analysis during the testing phase, thus improving test quality while reducing time consumption.
Solution Approach 2:
The patent implements self-service through automated test case generation and selection algorithms that independently analyze requirements, identify critical context parameters, and generate test cases without requiring extensive manual intervention. The system serves itself by using predefined risk models and business rules to automatically produce high-quality test cases, reducing both time consumption and manual effort.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for identifying test cases. One example process includes operations for identifying a mobile application to perform testing upon. A test environment and at least one risk situation associated with the mobile application are identified. For each of the at least one identified risk situations, at least one risk situation-relevant context parameter is identified. A standard operations path is created, as is at least one operations path-variant for each of the at least one identified risk situation-relevant context parameters. The corresponding operations path-variant is analyzed to identify a set of test cases for the context parameter, for each of the at least one identified context parameters.