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://github.com/KDEGroup/EALink.

Chenyuan Zhang

Xiamen University

Yanlin Wang

Sun Yat-sen University

China

Zhao Wei

Tencent

China

Yong Xu

Tencent

China

Juhong Wang

Tencent

China

Hui Li

Xiamen University

China

Rongrong Ji

Xiamen University

China