In automotive software development, as well as other domains, traceability between stakeholder requirements and system requirements is crucial to ensure consistency, correctness, and regulatory compliance. However, erroneous or missing traceability relationships often arise due to improper propagation of requirement changes or human errors in requirement mapping, leading to inconsistencies and increased maintenance costs. Existing approaches do not address traceability between stakeholder and system requirements, and are not validated on industrial data, where the links between requirements are established manually by engineers. Additionally, automotive requirements often exhibit variations in the way they are expressed, posing challenges for training-based approaches. Recent advancements in large language models (LLMs) provide new opportunities to address these challenges. In this paper, we introduce TVR, a requirement Traceability Validation and Recovery approach primarily targeting automotive systems, leveraging LLMs enhanced with retrieval-augmented generation (RAG). TVR is designed to validate existing traceability links and recover missing ones with high accuracy. The experimental results highlight the practical effectiveness of TVR in industrial settings, offering a promising solution for improving requirements traceability in complex automotive systems.

翻译：在汽车软件开发及其他领域中，利益相关者需求与系统需求之间的可追踪性对于确保一致性、正确性和法规遵从性至关重要。然而，由于需求变更的不当传递或需求映射过程中的人为错误，常常出现错误或缺失的可追踪性关系，导致不一致性并增加维护成本。现有方法未能解决利益相关者需求与系统需求之间的可追踪性问题，且未在工业数据上得到验证——在工业场景中，需求间的关联通常由工程师手动建立。此外，汽车需求在表达方式上常存在差异，这对基于训练的方法提出了挑战。大型语言模型（LLMs）的最新进展为解决这些挑战提供了新机遇。本文提出TVR，一种主要面向汽车系统的需求可追踪性验证与恢复方法，该方法利用检索增强生成（RAG）技术增强的LLMs。TVR旨在以高精度验证现有可追踪性链接并恢复缺失链接。实验结果突显了TVR在工业环境中的实际有效性，为改善复杂汽车系统中的需求可追踪性提供了有前景的解决方案。