MPLinker: Multi-template Prompt-tuning with adversarial training for Issue–commit Link recovery

Abstract

In recent years, the pre-training, prompting and prediction paradigm, known as prompt-tuning, has achieved significant success in Natural Language Processing (NLP). Issue–commit Link Recovery (ILR) in Software Traceability (ST) plays an important role in improving the reliability, quality, and security of software systems. The current ILR methods convert the ILR into a classification task using pre-trained language models (PLMs) and dedicated neural networks. These methods do not fully utilize the semantic information embedded in PLMs, failing to achieve acceptable performance. To address this limitation, we introduce a novel paradigm: Multi-template Prompt-tuning with adversarial training for issue–commit Link recovery (MPLinker). MPLinker redefines the ILR task as a cloze task via template-based prompt-tuning and incorporates adversarial training to enhance model generalization and reduce overfitting. We evaluated MPLinker on six open-source projects using a comprehensive set of performance metrics. The experiment results demonstrate that MPLinker achieves an average F1-score of 96.10%, Precision of 96.49%, Recall of 95.92%, MCC of 94.04%, AUC of 96.05%, and ACC of 98.15%, significantly outperforming existing state-of-the-art methods. Overall, MPLinker improves the performance and generalization of ILR models and introduces innovative concepts and methods for ILR. The replication package for MPLinker is available at https://github.com/WTU-intelligent-software-development/MPLinker.