Applications of Machine Learning in Requirements Traceability: A Systematic Mapping Study (S)

Abstract

Requirements traceability (RT) is crucial for requirement management and impact analysis of requirement change in software development. The applications of machine learning (ML) technologies to RT have received much attention. In this paper, we aim to provide the state-of-the-art progress of the studies on the intersection of ML and RT. A systematic mapping study (SMS) is conducted and 26 studies have been identified as primary studies. The results present 32 ML technologies and 7 enhancement strategies for establishing trace links. Besides, 46 datasets are utilized for validating the performance of these ML technologies. Additionally, the overall quality of these primary studies is at a good level. This study indicates that numerous studies have proved the potential of utilizing ML technologies for predicting emerging trace links in RT by utilizing existing traceability information. Moreover, open-source datasets are the most popular, which greatly improves the reproducibility of studies. However, there is still a gap between academia and industrial application because of the lack of industrial practice and guidance from practitioners.