Multi-dimensional Regression Test Impact Scoring
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Solution Overview
Problem
Existing regression testing methods are inefficient in optimizing time and effort for full coverage, as they typically focus on code changes without considering multi-dimensional aspects such as configuration, operational, and knowledge-based changes, leading to incomplete testing and increased overhead.
Innovation Solution
A system and method that utilize multi-dimensional modeling to identify and prioritize regression tests by mapping source code to functional testing entities, calculating a multi-dimensional impact score, and generating outputs that show impacted entities, enabling more granular and deep functional scenario test coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full coverage regression testing is performed, then testing completeness is improved, but testing time and effort increase
Solution Approach 1:
The patent segments the regression test suite into multiple test suites based on different dimensions (code changes, configuration changes, operational changes, knowledge changes). Each test suite focuses on a specific dimension, allowing the system to selectively execute only the relevant test suites for each change type, thereby reducing overall testing time while maintaining comprehensive coverage across all dimensions.
Solution Approach 2:
The patent implements a dynamic regression testing approach where the test selection is automatically adjusted based on the type and impact of changes detected in the software system. The system dynamically determines which test suites to execute by analyzing change dimensions, ensuring that testing scope adapts to the actual nature of changes rather than following a fixed comprehensive test plan.
2Ease of operation
If regression testing focuses only on code changes, then testing simplicity is improved, but testing comprehensiveness deteriorates
Solution Approach 1:
The patent creates a universal regression testing framework that handles multiple types of changes (code, configuration, operational, knowledge) through a single integrated system. The same multi-dimensional modeling and test suite selection mechanism works for all change types, providing comprehensive coverage without requiring separate simple testing processes for each dimension.
Solution Approach 2:
The patent extends the testing approach from a single dimension (code changes) to multiple dimensions by introducing configuration changes, operational changes, and knowledge changes as additional dimensions. This dimensional expansion allows the system to comprehensively cover all aspects of software changes while maintaining a structured and manageable testing process through multi-dimensional modeling.
3Measurement precision
If multi-dimensional modeling is implemented, then testing accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex multi-dimensional modeling into distinct, manageable components: code change detection, configuration change detection, operational change detection, and knowledge change detection. Each dimension is modeled separately with its own characteristics and testing implications, making the overall complex system more manageable and easier to implement while maintaining high measurement precision.
Data Source
AI summary
A system, method, and computer program product are provided for implementing optimal regression test strategies. In use, one or more functional testing entities are identified to be utilized to determine a scope associated with at least one regression test to be performed. The at least one regression test to be performed is associated with at least one change made to a functional system based on multi-dimensional modelling. Additionally, source code is mapped to the one or more functional testing entities, the source code being associated with the functional system. Further, a multi-dimensional impact score is calculated for each of the one or more functional testing entities resulting from the at least one change made to the functional system, based on the mapping of the source code to the one or more functional testing entities. Moreover, an output is generated showing impacted functional testing entities based on the multi-dimensional impact score for each of the one or more functional testing entities.