A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders

Software Requirement Document (RD) typically contains tens of thousands of individual requirements, and ensuring consistency among these requirements is a critical prerequisite for the success of software engineering projects. Automated detection methods can significantly enhance efficiency and reduce costs; however, existing approaches still face several challenges, including low detection accuracy on imbalanced data, limited semantic extraction due to the use of a single encoder, and poor performance in cross-domain transfer learning. To address these issues, this paper proposes a Transferable Software Requirement Conflicts Detection Framework based on SBERT and SimSCE, termed TSRCDF-SS. First, the framework employs two independent encoders named Sentence-BERT (SBERT) and Simple Contrastive Sentence Embedding (SimCSE) to generate sentence embeddings for requirement pairs, followed by a six-element concatenation strategy. Furthermore, the classifier is enhanced by incorporating a two-layer fully connected, alongside a hybrid loss function optimization strategy for feedforward neural network (FFNN) that integrates a variant of Focal Loss, domain-specific constraints, and a confidence-based penalty term. Finally, the framework synergistically integrates sequential and cross-domain transfer learning. Experimental results demonstrate that, compared with other advanced classical methods, our framework achieves an improvement ranging from 4.9% to 12.1% in macro-F1 and weighted-F1 under non-cross-domain conditions, and an average enhancement of 6% in macro-F1 under optimal cross-domain scenarios.