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MPlinker: Multi-template Prompt-tuning with adversarial training for issue-commit link recovery

JOURNAL OF SYSTEMS AND SOFTWARE 2025年 223卷

作者： Wang, Bangchao Deng, Yang Luo, Ruiqi Liang, Peng Bi, Tingting Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Wuhan Text Univ Engn Res Ctr Hubei Prov Clothing Informat Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China Univ Western Australia Daglish WA Australia

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://***/WTU-intelligent-software-development/ MPlinker.

关键词： Prompt-tuning issue-commit link recovery Pre-trained language model Natural language processing

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EAlink: An Efficient and Accurate Pre-trained Framework for issue-commit link recovery 38

EALink: An Efficient and Accurate Pre-trained Framework for ...

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38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Zhang, Chenyuan Wang, Yanlin Wei, Zhao Xu, Yong Wang, Juhong Li, Hui Ji, Rongrong Xiamen Univ Sch Informat Minist Educ China Key Lab Multimedia Trusted Percept & Efficient C Xiamen Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Guangdong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9798350329964

issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pre-trained models, to improve automatic issue-commit link recovery. Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EAlink for issue-commit link recovery. EAlink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EAlink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EAlink. Results show that EAlink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods. We provide our implementation and data at https://***/KDEGroup/EAlink.

关键词： issue-commit link recovery software traceability

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FRElinker: A Novel issue-commit link recovery Model Based on Feature Refinement and Expansion with Multi-Classifier Fusion 31

FRELinker: A Novel Issue-Commit Link Recovery Model Based on...

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31st Asia-Pacific Software Engineering Conference, APSEC 2024

作者： Wang, Bangchao He, Xinyu Wan, Hongyan Li, Xiaoxiao Zhu, Jiaxu Cao, Yukun School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

ISBN: (纸本)9798331534011

In the field of software traceability (ST), machine learning (ML) has become a common and effective method for automated issue-commit link recovery. The features extracted from issue and commit artifacts are composed of significantly different types of data, such as issue summaries, diff codes, and hashes. Such complex and diverse data poses a challenge to conventional ML methods. To overcome this challenge, we propose a novel model named FRElinker, which trains independent classifiers based on the type of issue-commit training data, fully leveraging the effectiveness of ML for single type data, and then fuses the classifiers. Specifically, we categorize the features into four types: textual features, code features, non-textual features, and similarity features, and extend text similarity features by adding hybrid textual similarity measures. And then, we use a ranking method to select the optimal classifiers corresponding to these four types of features. Among them, the optimal classifier for textual features is Gradient Boosting (GB), the optimal classifier for code features is Logistic Regression (LR), and the optimal classifier for non-textual features and similarity features is Random Forest (RF). Finally, we use a Bayesian optimization model to fuse these four classifiers. Experimental results show that our method outperforms competing methods Hybrid-linker and Deeplink in terms of Precision, Recall, and F-measure on six real-world open-source software (OSS) datasets, demonstrating significant performance advantages in complex and diverse data. © 2024 IEEE.

关键词： issue-commit link recovery machine learning software engineering software traceability

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EAlink: An Efficient and Accurate Pre-trained Framework for issue-commit link recovery 23

EALink: An Efficient and Accurate Pre-trained Framework for ...

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Chenyuan Zhang Yanlin Wang Zhao Wei Yong Xu Juhong Wang Hui Li Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

ISBN: (纸本)9798350329964

关键词： issue-commit link recovery

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MTlink: Adaptive multi-task learning based pre-trained language model for traceability link recovery between issues and commits

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第2期36卷

作者： Deng, Yang Wang, Bangchao Zhu, Qiang Liu, Junping Kuang, Jiewen Li, Xingfu Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Engn Res Ctr Hubei Prov Clothing Informat Wuhan Peoples R China

Traceability links between issues and commits (issue-commit links recovery (ILR)) play a significant role in software maintenance tasks by enhancing developers' observability in practice. Recent advancements in large language models, particularly pre-trained models, have improved the effectiveness of automated ILR. However, these models' large parameter sizes and extended training time pose challenges in large software projects. Besides, existing methods often overlook the association and distinction among artifacts, leading to the generation of erroneous links. To mitigate these problems, this paper proposes a novel link recovery method called MTlink. It utilizes multi-teacher knowledge distillation (MTKD) to compress the model and employs an adaptive multi-task strategy to reduce information loss and improve link accuracy. Experiments are conducted on four open-source projects. The results show that (i) MTlink outperforms state-of-the-art methods;(ii) The multi-teacher knowledge distillation maintains accuracy despite model size reduction;(iii) The adaptive multi-task tracing method effectively handles confusion caused by similar artifacts and balances each task. In conclusion, MTlink offers an efficient solution for ILR in software traceability. The code is available at https://***/records/10321150.

关键词： issue-commit link recovery Multi-teacher knowledge distillation Adaptive multi-task

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