Being aware of and understanding the relations between the requirements of a software system to its other artifacts is crucial for their successful development, maintenance and evolution. There are approaches to automatically recover this traceability information, but they fail to identify the actual relevant parts of the requirements. Recent large language model-based requirements classification approaches have shown to be able to identify aspects and concerns of requirements with promising accuracy. Therefore, we investigate the potential of those classification approaches for identifying irrelevant requirement parts for traceability link recovery between requirements and code.

We train the large language model-based requirements classification approach NoRBERT on a new dataset of requirements and their entailed aspects and concerns. We use the results of the classification to filter irrelevant parts of the requirements before recovering trace links with the fine-grained word embedding-based FTLR approach.

Two empirical studies show promising results regarding the quality of classification and the impact on traceability link recovery. NoRBERT can identify functional and user-related aspects in the requirements with an F₁-score of 84%. With the classification and requirements filtering, the performance of FTLR could be improved significantly and FTLR performs better than state-of-the-art unsupervised traceability link recovery approaches.