EmbC-Test: 如何利用LLMs和RAG加速嵌入式软件测试
EmbC-Test: How to Speed Up Embedded Software Testing Using LLMs and RAG
AI总结：
本文提出利用LLMs和RAG技术，通过生成自动测试用例，显著提高嵌入式软件测试效率，节省66%的测试时间并每小时生成270个测试用例。
摘要：
手动开发自动测试用例对于嵌入式C软件是一项繁重且耗时的任务，且难以扩展。随着软件发布周期的加快，验证过程在嵌入式开发流程中越来越成为瓶颈。本文提出了一种检索增强生成（RAG）管道，作为验证过程部分自动化的解决方案。通过将大型语言模型与项目特定的工件相结合，该方法减少了幻觉并提高了项目对齐性。一项工业评估显示，生成的测试用例在语法上完全正确，其中85%在运行时验证中通过。所提出的解决方案相比手动编写测试用例可节省多达66%的测试时间，并每小时生成270个测试用例。
英文摘要：
Manual development of automatic tests for embedded C software is a strenuous and time-consuming task that does not scale well. With the accelerating pace of software release cycles, verification increasingly becomes the bottleneck in the embedded development workflow. This paper presents a Retrieval-Augmented Generation (RAG) pipeline as a solution for partial automation of the verification process. By grounding a large language model in project-specific artifacts, the approach reduces hallucinations and improves project alignment. An industrial evaluation showed that the generated tests are 100 % syntactically correct, with 85 % successfully passing runtime validation. The proposed solution has the potential to save up to 66 % of the testing time compared to manual test writing while generating 270 tests per hour.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
