Adaptive Testing Benchmark Generation for Multi-Architecture Systems
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Solution Overview
Problem
Conventional testing benchmarks are not adaptable to various hardware and software architectures, limiting their effectiveness in evaluating product performance across different systems.
Innovation Solution
A testing benchmark generation method and system that utilizes a prompt generation module, an inference module with a large language model, and a profiling module to generate, verify, execute, and analyze code snippets, ensuring they meet specific constraints, and add them to testing benchmark data, thereby creating benchmarks applicable to diverse architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional testing benchmarks are used, then they can evaluate product performance, but they cannot cover all specific hardware and software architectures
Solution Approach 1:
The system dynamically adjusts prompt parameters and constraints based on the target hardware and software architecture being tested. By changing the parameters of the benchmark generation process (such as performance metrics, test scenarios, and evaluation criteria) to match different architectural characteristics, the system achieves both broad adaptability and reliable evaluation accuracy for each specific architecture type.
Solution Approach 2:
The benchmark generation process is made dynamic and adaptive rather than static. The system continuously adjusts the testing benchmarks based on feedback from the target architecture, allowing the same system to generate architecture-specific benchmarks on-demand. This dynamic approach enables the system to maintain high evaluation accuracy across diverse hardware and software platforms without requiring pre-defined fixed benchmarks for each architecture.
2Adaptability or versatility
If user-defined testing benchmarks are created, then they can be customized, but they require significant manual effort and expertise
Solution Approach 1:
The system enables automated self-service benchmark generation where the AI model automatically creates customized testing benchmarks based on input requirements about the target architecture. Users simply provide high-level specifications (such as architecture type and performance focus areas), and the system autonomously generates detailed, customized benchmarks without requiring manual creation efforts or deep expertise in benchmark development methodologies.
Solution Approach 2:
An AI-based intermediary system acts as a bridge between user requirements and complex benchmark generation. This intermediary automatically translates simple user inputs into sophisticated, architecture-specific testing benchmarks, eliminating the need for users to directly engage in the complex process of benchmark creation while still achieving highly customized results tailored to specific hardware and software platforms.
3Adaptability or versatility
If comprehensive testing benchmarks are generated, then they can cover all architectures, but the generation time and computational resources increase
Solution Approach 1:
The system generates benchmarks on-demand for specific target architectures rather than pre-generating comprehensive benchmarks for all possible architectures in advance. By implementing partial action (generating only when needed) and excessive action (capable of generating for any architecture on-demand), the system achieves broad architectural coverage without incurring the time cost of pre-generating all possible benchmarks, as each benchmark is created only when required for a specific testing scenario.
Data Source
AI summary
A testing benchmark generation method includes providing a product, generating prompt data of the product by a prompt generation module, generating a code snippet by a large language model according to the prompt data, semantically checking the code snippet by an inference module for determining if the code snippet is successfully verified, compiling the code snippet and executing the code snippet to drive the product after the code snippet is successfully verified, analyzing an execution result corresponding to the code snippet after the code snippet is executed under the product, comparing the execution result corresponding to the code snippet with at least one constraint of the product by a profiling module for determining if the code snippet matches the at least one constraint, and adding the code snippet to testing benchmark data of a testing benchmark generation module.