Enhancing Unsupervised Requirements Traceability with Sequential Semantics

Abstract

Requirements traceability provides important support throughout all software life cycle; however, creating such links manually is time-consuming and error-prone. Supervised automated solutions use machine learning or deep learning techniques to generate trace links, but require large labeled dataset to train an effective model. Unsupervised solutions as word embedding approaches can generate links by capturing the semantic meaning of artifacts and are gaining more attention. Despite that, our observation revealed that, besides the semantic information, the sequential information of terms in the artifacts would provide additional assistance for building the accurate links. This paper proposes an unsupervised requirements traceability approach (named S2Trace) which learns the Sequential Semantics of software artifacts to generate the trace links. Its core idea is to mine the sequential patterns and use them to learn the document embedding representation. Evaluation is conducted on five public datasets, and results show that our approach outperforms three typical baselines. The modeling of sequential information in this paper provides new insights into the unsupervised traceability solutions, and the improvement in the traceability accuracy further proves the usefulness of the sequential information.