Dynamic Test Scenario Generation via Risk Analysis
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Solution Overview
Problem
Current code testing methods are inefficient due to the repetitive execution of static test scenarios, which become outdated and redundant, leading to unnecessary testing of non-problematic sections of software, while failing to effectively target areas with high error probabilities.
Innovation Solution
A method for dynamically generating test scenarios based on risk analysis, utilizing clustering techniques to identify and prioritize code components with high error probabilities, thereby creating custom-made test scenarios that focus on changed areas and adapt to current product quality, reducing manual maintenance costs and improving defect discovery efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static test scenarios are executed repeatedly, then testing coverage is maintained, but testing efficiency decreases and time is wasted on non-problematic areas
Solution Approach 1:
The patent transforms static test scenarios into dynamic test scenarios that automatically adapt to code changes. The system continuously updates test scenarios based on detected code modifications, ensuring testing focus shifts from repetitive static execution to targeted dynamic testing of changed areas only.
Solution Approach 2:
The system changes the parameter of test scenario selection from fixed/static to variable/dynamic. By monitoring code attributes and detecting changes, the system adjusts which test scenarios should be executed based on current code state, transforming the testing approach from rigid repetition to flexible adaptation.
2Reliability
If all code components are tested uniformly, then comprehensive coverage is achieved, but testing time increases and high-error areas are not prioritized
Solution Approach 1:
The patent applies local quality by differentiating testing intensity based on code component characteristics. Instead of uniform testing, the system identifies code components with higher error probabilities (through code attribute analysis) and prioritizes testing efforts on those specific areas, applying more rigorous testing where needed while reducing effort on lower-risk areas.
Solution Approach 2:
The system segments the codebase into distinct code components based on their attributes and error probabilities. By dividing the testing scope into segments according to risk levels, the system can allocate testing resources more efficiently, focusing on high-risk segments while maintaining awareness of overall coverage through the segmented structure.
3Measurement precision
If test scenarios are manually maintained, then accuracy is improved, but maintenance cost and complexity increase
Solution Approach 1:
The patent implements self-service by enabling the testing system to automatically maintain itself through code change detection. The system monitors code attributes, detects modifications, and autonomously updates test scenarios without human intervention. This eliminates manual maintenance while preserving test accuracy, as the system self-adjusts based on actual code changes.
Solution Approach 2:
The system establishes a feedback loop where code changes are detected, analyzed through attribute comparison, and used to automatically update test scenarios. This feedback mechanism ensures test scenarios remain accurate and current without requiring manual maintenance, as the system continuously adapts to code evolution through automated detection and adjustment.
Data Source
AI summary
Embodiments include a method for generation of test scenarios based on risk analysis. The method includes receiving a first set of code test scenarios, the first set of code test scenarios configured to test one or more first code components, the first code components each including first code attributes. The method also includes determining a probability of finding defects in the first code components based on the first code attributes. The method also includes generating a second set of code test scenarios based on the determining the probability of finding defects in the first code components based on the first code attributes, where the second set of one or more test scenarios is configured to test one or more second code components based on the second code attributes.