Cross-level Requirement Traceability: A Novel Approach Integrating Bag-of-Words and Word Embedding for Enhanced Similarity Functionality

TL;DR

This paper introduces a new method for requirement traceability that combines Bag-of-Words and word embeddings to improve similarity detection between requirements at different abstraction levels, enhancing automation and accuracy.

Contribution

The paper presents a novel approach integrating Bag-of-Words, TF-IDF, and advanced word embeddings with an improved similarity measure for cross-level requirement linking.

Findings

Significant improvement in traceability accuracy on three datasets.

Approximately 18.4% increase in F2 score on one dataset.

Enhanced similarity function outperforms traditional methods.

Abstract

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.