Requirement traceability is the process of identifying the inter-dependencies between requirements. It poses a significant challenge when conducted manually, especially when dealing with requirements at various levels of abstraction. In this work, we propose a novel approach to automate the task of linking high-level business requirements with more technical system requirements. The proposed approach begins by representing each requirement using a Bag of-Words (BOW) model combined with the Term Frequency-Inverse Document Frequency (TF-IDF) scoring function. Then, we suggested an enhanced cosine similarity that uses recent advances in word embedding representation to correct traditional cosine similarity function limitations. To evaluate the effectiveness of our approach, we conducted experiments on three well-known datasets: COEST, WARC(NFR), and WARC(FRS). The results demonstrate that our approach significantly improves efficiency compared to existing methods. We achieved better results with an increase of approximately 18.4% in one of the datasets, as measured by the F2 score.

翻译：需求可追踪性是识别需求间相互依赖关系的过程。当手动执行时，尤其是在处理不同抽象层级的需求时，这项工作面临重大挑战。在本研究中，我们提出一种新颖方法，用于自动化链接高层级业务需求与更具技术性的系统需求的任务。所提出的方法首先使用词袋模型结合词频-逆文档频率评分函数来表示每个需求。随后，我们提出一种增强的余弦相似度计算方法，该方法利用词嵌入表示的最新进展来修正传统余弦相似度函数的局限性。为评估我们方法的有效性，我们在三个知名数据集上进行了实验：COEST、WARC(NFR) 和 WARC(FRS)。结果表明，与现有方法相比，我们的方法显著提升了效率。在其中一个数据集中，我们通过F2分数衡量取得了更好的结果，提升幅度约为18.4%。