How to use domestic large models for automated testing and quality assurance?

When there is a need to improve the efficiency of automated testing and quality assurance capabilities, domestic large models can empower core links such as test case generation, script optimization, and defect location through natural language processing, code generation, and intelligent analysis technologies. Test case generation: Based on requirement documents or product descriptions, automatically generate structured test cases covering functional points, abnormal scenarios, and boundary conditions, reducing manual writing costs. Test script optimization: For mainstream testing languages such as Python and Java, intelligently generate or optimize automated scripts to improve script readability and execution stability. Defect analysis: By analyzing test logs and error information, automatically locate the root cause of defects and provide repair suggestions, shortening the problem troubleshooting cycle. In practical applications, it is recommended to prioritize domestic large models that support Chinese semantic understanding (such as Baidu Wenxin Yiyan, Alibaba Tongyi Qianwen), combine with test management tools (such as TestRail) to achieve process closed-loop, and gradually verify the accuracy of model output to ensure test quality.