Exploring Semantics of Software Artifacts to Improve Requirements Traceability Recovery: A Hybrid Approach

Continuously maintaining software requirements traceability links is essential for managing and evolving software systems. Due to development pressure, traceability links are usually missing during the early development phase in practice, and thus many information retrieval-based approaches have been proposed to automatically recover the traceability links. However, such approaches typically calculate textual similarities among software artifacts without considering specific features of different software artifacts, leading to less accurate results. In this paper, we propose a hybrid approach to recover requirements traceability links, which combines machine learning and logical reasoning to explore features of use cases and code. On one hand, our approach engineers features of use cases and code by taking into account their semantics, based on which a classifier is trained by using supervised learning algorithms. On the other hand, we investigate and leverage the structural information of code to incrementally discover traceability links by defining a list of reasoning rules. We have carried out a series of experiments to compare our approach with state-of-the-art methods, the results of which show that our approach significantly outperforms others.