MPLinker: Multi-template Prompt-tuning with Adversarial Training for Issue-commit Link Recovery

TL;DR

This paper introduces MPLinker, a novel prompt-tuning approach with adversarial training for issue-commit link recovery in software traceability, significantly improving accuracy and generalization over existing methods.

Contribution

It proposes a multi-template prompt-tuning framework combined with adversarial training to enhance ILR performance and robustness, addressing limitations of prior neural network-based approaches.

Findings

Achieves an average F1-score of 96.10% on six open-source projects.

Outperforms existing state-of-the-art ILR methods in multiple metrics.

Demonstrates improved model generalization and reduced overfitting.

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, resulting in not achieving 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