A Cross-Level Requirement Trace Link Update Model Based on Bidirectional Encoder Representations from Transformers

Abstract

Cross-level requirement trace links (i.e., links between high-level requirements (HLRs) and low-level requirements (LLRs)) record the top-down decomposition process of requirements and support various development and management activities (e.g., requirement validation). Undoubtedly, updating trace links synchronously with requirement changes is critical for their constant availability. However, large-scale open-source software that is rapidly iterative and continually released has numerous requirements that are dynamic. These requirements render timely update of trace links challenging. To address these problems, in this study, a novel deep-learning-based method, deep requirement trace analyzer fusing heterogeneous features (DRAFT), was proposed for updating trace links between various levels of requirements. Considering both the semantic information of requirement text descriptions and the process features based on metadata, trace link data accumulated in the early stage are comprehensively used to train the trace link identification model. Particularly, first, we performed second-phase pre-training for the bidirectional encoder representations from transformers (BERT) language model based on the project document corpus to realize project-related knowledge transfer, which yields superior text embedding. Second, we designed 11 heuristic features based on the requirement metadata in the open-source system. Based on these features and semantic similarity between HLRs and LLRs, we designed a cross-level requirement tracing model for new requirements. The superiority of DRAFT was verified based on the requirement datasets of eight open-source projects. The average F1 and F2 scores of DRAFT were 69.3% and 76.9%, respectively, which were 16.5% and 22.3% higher than baselines. An ablation experiment proved the positive role of two key steps in trace link construction.