摘要
Abstract
Aiming at the problems of low feasibility,correctness,alignment with requirements,and coverage of test cases generated by basic large models,an improvement method based on retrieval enhanced generation,prompt language engineering,and large model fine-tuning is proposed.Firstly,combining retrieval enhanced generation with large model language to supplement the generated results of the large model with external data;Secondly,establish an optimization system for prompt words,utilizing dynamic prompt adjustment and prompt language templating to make the output results more accurate;Finally,further fine tune the pre trained language model,but only adjust a small number of embedded words,rather than updating the model parameters on a large scale to reduce resource investment.The experiment shows that the proposed method improves feasibility,correctness,requirement alignment,and requirement coverage by 27.92%,33.83%,45.3%,and 42%,respectively,compared to the basic large model.关键词
大模型/检索增强/提示语工程/大模型微调/测试用例生成Key words
large model/retrieval-augmented generation/prompt engineering/fine-tuning large models/test case generation分类
信息技术与安全科学
