Parallelizable Data-Driven SIMD Test Generation
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Solution Overview
Problem
Existing software testing methodologies in modern enterprise computing environments only provide for data-driven testing or parallelization, failing to effectively leverage both data-driven and parallelization-based SIMD testing, leading to inefficiencies in executing the same tests across multiple parameters and inputs.
Innovation Solution
The method involves designing a base abstract class with inheriting classes that derive a data source key, accessing a test data source, and generating SIMD tests by combining inheritance mechanisms for code and data duplication, unit testing, integration testing, and end-to-end testing methodologies, allowing for parallel execution and efficient test data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data-driven testing is performed sequentially on multiple parameters and inputs, then test coverage is comprehensive, but execution time increases significantly
Solution Approach 1:
The patent segments the monolithic test execution into multiple independent test cases, each handling a specific parameter combination. By dividing the test suite into separable units that can be executed independently, the system enables parallel processing across multiple CPU cores, thereby reducing overall execution time while maintaining comprehensive test coverage.
Solution Approach 2:
The patent implements dynamic test generation and execution by creating test cases at runtime based on input parameters. The system dynamically adapts the test execution plan to available computational resources, allowing tests to be distributed and executed in parallel across multiple processors, thus reducing execution time without sacrificing coverage.
2Reliability
If tests are rewritten for different parameters and inputs, then test specificity increases, but development effort and complexity increase
Solution Approach 1:
The patent creates a universal test case template that can handle multiple parameters and inputs through parameterization. Instead of writing separate tests for each scenario, a single test framework accepts different parameters dynamically, maintaining high test specificity while significantly reducing development effort and avoiding code duplication.
Solution Approach 2:
The patent employs parameter-driven test generation where test cases are defined with placeholder parameters that are populated at runtime. This approach allows the same test structure to be reused across different scenarios by simply changing the parameter values, thereby maintaining test specificity without increasing development complexity.
3Productivity
If parallelization is implemented without data-driven approach, then execution speed improves, but test adaptability to different inputs decreases
Solution Approach 1:
The patent performs preliminary setup by defining parameterized test templates and data sources before execution. This preliminary configuration enables the system to rapidly instantiate specific test cases during parallel execution, maintaining both high execution speed and adaptability to different inputs through pre-prepared flexible test structures.
4Productivity
If SIMD testing methodology is used, then computational efficiency improves, but implementation complexity increases
Solution Approach 1:
The patent introduces an intermediary layer that translates high-level parameterized test definitions into optimized SIMD instructions. This intermediary translation layer handles the complexity of SIMD implementation details, allowing the rest of the system to work with simple parameterized tests while still achieving computational efficiency through vectorized execution.
Data Source
AI summary
Disclosed herein are methods, systems, and processes to generate and perform parallelizable data-driven single instruction multiple data (SIMD) tests. A base abstract class that defines shared testing parameters for tests to be performed on an application is designated. Inheriting classes of the base abstract class are defined and a data source key is derived from the inhering classes. A test data source is accessed to perform the tests on the application and a result of tests is generated based on the data source key.