A Method for Intelligent Optimization Algorithms to Automatically Generate LLM Test Data

As AI products become more and more complex, traditional methods of manual test data generation are facing problems such as high cost, low efficiency, and insufficient test coverage, which affect the quality and stability of products. In order to solve the above problems, this paper proposes a method for automatic generation of AI (LLM) test data by intelligent optimization algorithm, and applies it to practical cases to carry out more detailed research. Experiments show that after a company introduces an intelligent optimization algorithm to automatically generate AI test data, based on this method, the test data generation time of its software has been reduced from 80.16 hours to 20.25 hours. And at the same time, the test coverage has increased from 60.32 % to 95.10 %, and the defect discovery rate has also increased from 65.51 % to 90.17 %. It can be seen that this method can significantly improve the test efficiency, optimize the test cost, and enhance the stability and quality of the software. It has been proved that intelligent optimization algorithms can be reasonably applied to automatically generate software test data, and then form a reliable method to provide enterprises with more accurate and efficient software testing solutions, and provide other companies with valuable optimization experience.