A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders
Yizheng Wang, Tao Jiang, Jinyan Bai, Zhengbin Zou, Tiancheng Xue, Nan Zhang, Jie Luan
TL;DR
This paper introduces a new framework for detecting conflicts in software requirements using dual encoders and transfer learning, improving accuracy and cross-domain performance.
Contribution
The novel framework combines dual encoders (SBERT and SimCSE) with a hybrid loss function and transfer learning to enhance conflict detection in software requirements.
Findings
The framework achieves 4.9% to 12.1% improvement in macro-F1 and weighted-F1 under non-cross-domain conditions.
It shows an average 6% enhancement in macro-F1 under optimal cross-domain scenarios.
Abstract
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…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Topic Modeling