Test Case Prioritization Using Ensemble Model Risk Index
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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, involving a weighted aggregation of parameters like test case count, defects, and other features, and training an ensemble model to identify likely unnecessary or redundant test cases, thereby generating scores for each test case and outputting a ranked list for execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are executed to ensure comprehensive testing coverage, then testing reliability is improved, but execution time and computational resources increase due to redundant or unwanted test cases
Solution Approach 1:
The system performs preliminary analysis of test cases by training an ensemble model on historical test artifacts before actual execution. This preliminary action identifies and filters out redundant or unwanted test cases in advance, so that only relevant test cases are executed during the actual testing phase, reducing execution time while maintaining reliability
Solution Approach 2:
The system uses feedback from historical test execution results (test artifacts) to train the ensemble model. This feedback mechanism allows the system to learn which test cases are effective and which are redundant, enabling intelligent selection for future testing cycles that reduces unnecessary execution time
2Reliability
If all test cases are executed to maximize testing coverage, then defect detection capability is improved, but productivity decreases due to execution of unwanted or redundant test cases
Solution Approach 1:
The system applies local quality by differentiating the treatment of individual test cases based on their specific characteristics and historical performance. Instead of treating all test cases uniformly, the ensemble model evaluates each test case's likelihood of detecting defects based on its unique properties and historical data, executing only those with high defect detection potential
Solution Approach 2:
The system changes the parameter of test case selection from binary (execute or skip) to a ranked prioritization system. The ensemble model generates priority scores for each test case based on multiple parameters including historical execution results, defect detection effectiveness, and redundancy analysis, enabling selective execution that optimizes productivity while maintaining defect detection capability
3Device complexity
If test case prioritization is performed without using historical data, then implementation complexity is reduced, but prioritization accuracy decreases leading to redundant test execution
Solution Approach 1:
The system uses copying by creating a virtual representation (ensemble model) of historical test execution patterns. Instead of directly analyzing raw historical data in complex ways, the model copies the essential patterns and relationships from historical test artifacts, making the prioritization process more manageable while improving accuracy through learned patterns from past performance
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.