ML Genetic Algorithm for IT Asset Test Plan Generation
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Solution Overview
Problem
Conventional testing automation approaches are inefficient in generating comprehensive testing plans for information technology assets, as they rely on randomized combinations and cannot enumerate all possible testing scenarios, leading to incomplete coverage and inefficiency.
Innovation Solution
The implementation of machine learning-based genetic algorithms that dynamically generate and execute testing plans by selecting and modifying plans based on scores, using behavior trees and genetic operators like crossover and mutation to achieve specific testing goals, ensuring adaptive and goal-based testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional randomized testing automation approaches are used, then the testing process is simple to implement, but the test coverage is incomplete and efficiency is low
Solution Approach 1:
The patent replaces conventional randomized mechanical testing approaches with machine learning-based genetic algorithms. The system uses ML models to analyze software code, identify critical testing scenarios, and generate optimized test plans that systematically cover all possible test cases rather than relying on random selection, thereby improving both coverage completeness and testing efficiency.
Solution Approach 2:
The patent changes the parameters of test plan generation by introducing multiple dimensions including code complexity metrics, test scenario priorities, resource constraints, and time deadlines. The genetic algorithm optimizes these parameters to generate test plans that maximize coverage while adhering to practical constraints, resolving the contradiction between comprehensive coverage and testing efficiency.
2Reliability
If machine learning-based genetic algorithms are used to generate testing plans, then test coverage and efficiency are improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex test plan generation process into distinct modular components: code analysis module, scenario identification module, genetic algorithm optimization module, and test execution module. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable despite the advanced ML-based approach.
Solution Approach 2:
The patent introduces an intermediary layer between the code base and the test execution, consisting of the genetic algorithm optimizer and ML models. This intermediary automatically translates code characteristics into optimized test plans, shielding users from the underlying system complexity while delivering comprehensive test coverage.
3Reliability
If all possible testing scenarios are enumerated, then complete test coverage is achieved, but the time and resources required increase significantly
Solution Approach 1:
The patent applies partial action by using genetic algorithms to identify and prioritize the most critical test scenarios from the set of all possible tests. Rather than executing every conceivable test case, the system selects a optimized subset that provides maximum coverage value, thereby reducing testing time while maintaining completeness of essential coverage.
Solution Approach 2:
The patent performs preliminary analysis of the software code base using machine learning models to identify critical paths, high-risk modules, and important test scenarios before generating the actual test plan. This preliminary action enables the system to focus testing efforts on the most valuable areas, avoiding waste of time on less critical tests while ensuring complete coverage of essential functionality.
Data Source
AI summary
An apparatus comprises a processing device configured to determine one or more testing goals for testing to be performed on one or more of a plurality of information technology assets of an information technology infrastructure and to select two or more of a plurality of testing plans based at least in part on scores assigned to respective ones of the plurality of testing plans, the assigned scores characterizing whether respective ones of the plurality of testing plans meet the determined one or more testing goals. The processing device is further configured to generate, utilizing one or more machine learning-based genetic algorithms that take as input the selected two or more testing plans, one or more additional testing plans, and to execute the one or more additional testing plans on the one or more of the plurality of information technology assets of the information technology infrastructure.