Enhancing Requirements Traceability Link Recovery: A Novel Approach with T-SimCSE

2026-03-12

AI Summary

This work addresses the limitations of existing automated requirement traceability link recovery approaches, which typically rely on substantial labeled data and achieve only modest accuracy—conditions rarely met in real-world scenarios where labeled data are scarce. To overcome this challenge, the paper proposes T-SimCSE, a novel method that leverages the unsupervised pre-trained language model SimCSE to compute semantic similarity between requirements and target artifacts. It further introduces a new specificity metric to re-rank candidate links, enabling the generation of high-precision top-K traceability links without any labeled data. Experimental results across ten public datasets demonstrate that T-SimCSE significantly outperforms state-of-the-art methods in both recall and mean average precision (MAP).

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Requirements traceability plays an important role in ensuring software quality and responding to changes in requirements. Requirements trace links (such as the links between requirements and other software artifacts) underpin the modeling and implementation of requirements traceability. With the rapid development of artificial intelligence, more and more pre-trained language models (PLMs) techniques are applied to the automatic recovery of requirements trace links. However, the requirements traceability links recovered by these approaches are not accurate enough, and many approaches require a large labeled dataset for training. Currently, there are very few labeled datasets available. To address these limitations, this paper proposes a novel requirements traceability link recovery approach called T-SimCSE, which is based on a PLM -- SimCSE. SimCSE has the advantages of not requiring labeled data, having broad applicability, and performing well. T-SimCSE firstly uses the SimCSE model to calculate the similarity between requirements and target artifacts, and employs a new metric (i.e. specificity) to reorder those target artifacts. Finally, the trace links are created between the requirement and the top-K target artifacts. We have evaluated T-SimCSE on ten public datasets by comparing them with other approaches. The results show that T-SimCSE achieves superior performance in terms of recall and Mean Average Precision (MAP).

Problem

• requirements traceability
• trace link recovery
• pre-trained language models
• labeled dataset scarcity
• software artifacts

Innovation

• T-SimCSE
• requirements traceability
• SimCSE
• unsupervised learning
• specificity metric