Test Strategy Optimizer for Software Regression Testing
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Solution Overview
Problem
Regression testing in software applications is resource-intensive and often unnecessary, as existing tools fail to efficiently identify which processes and artifacts are affected by changes, leading to extensive testing of non-essential components.
Innovation Solution
A Test Strategy Optimizer tool that analyzes the criticality of processes and software changes, calculating an optimized test scope by determining which processes and artifacts need to be tested, based on their importance and interdependencies, to reduce unnecessary testing efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regression testing is performed on the entire system after every modification, then the reliability of the software is improved, but the time and resources required for testing increase significantly
Solution Approach 1:
The patent segments the regression test scope by identifying and separating affected processes and artifacts from unaffected ones. Instead of testing the entire system, the method divides the test set into relevant subsets based on change impact analysis, thereby reducing testing time while maintaining reliability for critical areas.
Solution Approach 2:
The patent applies local quality by directing enhanced testing focus to specific areas that are actually affected by changes. The test strategy optimizes resource allocation by applying stricter testing criteria to impacted processes and artifacts, while reducing or eliminating testing in unaffected areas, thus improving overall efficiency without compromising reliability where needed.
2Productivity
If risk-based subsets are defined for testing, then the testing resources are reduced, but the testing coverage may miss affected artifacts in non-critical processes
Solution Approach 1:
The patent performs preliminary change impact analysis before defining test subsets. By pre-identifying which artifacts and processes are affected by modifications, the method ensures that test coverage is determined based on actual impact rather than arbitrary risk categorization, preventing missed artifacts in non-critical processes while maintaining efficient resource usage.
Solution Approach 2:
The patent implements feedback mechanisms that continuously update the test strategy based on change impact analysis results. The system monitors which artifacts are affected by changes and adjusts the test scope accordingly, ensuring that even artifacts in non-critical processes are included if they are impacted by the modification, thereby maintaining comprehensive coverage while optimizing resource allocation.
3Reliability
If all artifacts are tested regardless of their relationship to the change, then the test coverage is maximized, but the test effort becomes unnecessarily extensive
Solution Approach 1:
The patent extracts and removes unaffected artifacts from the regression test scope through change impact analysis. By identifying artifacts that have no relationship to the applied changes, the method takes them out of the test set, reducing test scope complexity while maintaining coverage of all relevant artifacts that could be affected by the modification.
4Loss of time
If the test scope is reduced to only critical processes, then the testing time is reduced, but artifacts affected by changes in less critical processes are not tested
Solution Approach 1:
The patent performs preliminary change impact analysis to identify all affected artifacts regardless of process criticality. This pre-analysis ensures that even artifacts in less critical processes are detected and included in the test scope if they are impacted by changes, preventing missed coverage while avoiding unnecessary testing of completely unaffected artifacts, thus optimizing testing time without sacrificing reliability.
Data Source
AI summary
A test strategy optimizer for minimizing the impact of software update correctness testing is provided for software having several processes. Each process may have a criticality level associated with it and a test effort level associated with it. An update analyzer may determine what functions are modified and a dependency analyzer may determine what processes are connected to modifications, and thus require testing. User input may specify global test parameters related to test time, test completeness per criticality level, test iterations, etc. The test strategy optimizer will then take the process parameters, process dependencies, and user criteria to calculate the most efficient test procedure and provide information about the same. The test strategy optimizer may also provide adjustment tools, so users may modify parameters, based on the resulting information.