摘要
随着“软件定义汽车”的发展,汽车软件功能的复杂性和快速开发需求对电控系统验证提出了更高的要求。当前,电控系统软件功能的测试流程图开发主要依赖人工方式,效率低且存在人为因素影响。文中详细描述了汽车验证电控系统中的测试用例自动生成任务及其面临的挑战,并提出了一种基于大语言模型(LLM)的自动生成测试流程图方法,以提高开发效率并减少人力成本。该方法包括构建领域任务数据集和选择合适场景的大模型应用路线。在实验中探讨了基于传统语言模型微调和大语言模型API适配两种技术路线的优劣,并通过实验验证了不同的大模型API在测试用例生成任务上的表现,以及提示工程技术对大模型API的提升效果。提出了一种高效的自动生成汽车测试流程图的方法,展示了大语言模型在提升汽车软件测试效率中的潜力。
With the development of“software-defined vehicles”,the complexity of automotive software functions and the demand for rapid development have imposed higher requirements on the verification of electronic control systems.Currently,the development of test flow charts for electronic control system software functions mainly relies on manual methods,which are inefficient and susceptible to human factors.This paper details the task and challenges of automatic test case generation in automotive electronic control system verification and proposes an automatic test flow chart generation method based on large language models(LLM)to improve development efficiency and reduce labor costs.The method includes constructing domain task datasets and selecting appropriate LLM application routes.The study explores the advantages and disadvantages of two technical routes:traditional language model fine-tuning and LLM API adaptation.Experiments validate the performance of different LLM APIs in test case generation tasks and the effectiveness of prompt engineering techniques in enhancing LLM API performance.In summary,this paper proposes an efficient method for automatically generating automotive test flow charts,demonstrating the potential of LLMs in improving the efficiency of automotive software testing.
作者
李占旗
吴新维
张蕾
刘全周
谢辉
熊德意
LI Zhanqi;WU Xinwei;ZHANG Lei;LIU Quanzhou;XIE Hui;XIONG Deyi(CATARC(Tianjin)Automotive Engineering Research Institute Co.,Ltd.,Tianjin 300300,China;College of Intelligence and Computing,Tianjin University,Tianjin 300350,China;School of Mechanical Engineering,Tianjin University,Tianjin 300354,China;China Automotive Technology and Research Center Co.,Ltd.,Tianjin 300300,China)
出处
《计算机科学》
CSCD
北大核心
2024年第12期63-70,共8页
Computer Science
基金
国家重点研发计划(2021YFB3202204)。
关键词
汽车领域应用
大语言模型
提示工程
Automotive applications
Large language models
Prompt engineering
