Test Case Prioritization via Risk Index and Ensemble Model
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Solution Overview
Problem
The management of software application test cases is inefficient due to uncertainties in requirements, prioritization, and scheduling, leading to the execution of redundant or unwanted test cases, which increases product backlog and diminishes the return on investment for automated test scripting tools.
Innovation Solution
Prioritizing test cases using a risk index derived from test artifacts generated on legacy software versions, employing an ensemble model to identify likely unnecessary or redundant test cases, and generating scores for each test case to optimize execution order, thereby reducing delays and conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are automated and executed repeatedly to increase testing coverage, then detection capability is improved, but management time and effort increase
Solution Approach 1:
The system performs preliminary analysis by executing a subset of test cases to generate test artifacts before full testing begins. These artifacts are used to train models that predict test case outcomes, allowing for intelligent prioritization and selection of test cases to execute first, thereby reducing overall management time while maintaining detection capability.
Solution Approach 2:
The system implements feedback loops where test artifacts from executed test cases are used to train predictive models. These models provide feedback on which test cases are most likely to detect defects, enabling dynamic adjustment of test execution priorities and reducing the time needed to manage and execute comprehensive test suites.
2Reliability
If all test cases are executed to ensure comprehensive coverage, then detection completeness is improved, but computational resources are wasted on redundant test cases
Solution Approach 1:
Instead of executing all test cases, the system uses predictive models to identify and execute only the most critical test cases that are most likely to detect defects. This partial action approach maintains detection completeness by focusing resources on high-value test cases while avoiding wasted computational effort on redundant or low-value tests.
Solution Approach 2:
The system employs self-service mechanisms where executed test cases automatically generate test artifacts that train predictive models. These models then autonomously determine which test cases should be executed next, eliminating the need for manual intervention and optimizing computational resource allocation based on historical performance data.
3Productivity
If test cases are prioritized based on historical data, then execution efficiency is improved, but model training complexity increases
Solution Approach 1:
The system segments the test case management process into distinct phases: executing a subset of test cases to generate artifacts, training predictive models on these artifacts, and using the models to prioritize future test execution. This segmentation reduces overall complexity by breaking down the complex task of intelligent test selection into manageable, sequential steps that can be implemented progressively.
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer-storage media, for prioritizing test cases. Processes may include obtaining test artifacts that were generated based on testing one or more legacy versions of a software application using multiple test cases, generating a risk index based at least on the test artifacts that were generated based on testing the one or more legacy versions of the software application using the multiple test cases, and training an ensemble model that is configured to identify likely unnecessary or redundant test cases in connection with in testing an updated version of the software application, based at least on the risk index.