EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery

TL;DR

EALink is a novel pre-trained framework that significantly improves issue-commit link recovery accuracy while reducing training and inference costs, addressing limitations of previous deep learning approaches in large software projects.

Contribution

We introduce EALink, a lightweight and efficient pre-trained model that outperforms existing methods in accuracy and efficiency for issue-commit link recovery.

Findings

EALink outperforms state-of-the-art methods by 15.23%-408.65%.

EALink requires fewer parameters and less computational overhead.

Extensive experiments validate EALink's effectiveness across large datasets.

Abstract

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 pretrained 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.