一句话：实证表明迁移学习能显著提升预训练深度程序修复模型在工业 Kotlin 项目上的缺陷修复性能。
该研究关注 Samsung MX 团队从 Java 迁移至 Kotlin 时，Kotlin 缺陷修复数据不足，导致预训练深度自动程序修复（DL-APR）模型难以充分发挥作用。作者利用开源与工业 Kotlin 缺陷修复数据集进行迁移学习，并迁移工业（非缺陷）bug 修复数据集知识，同时分析嵌入向量与重叠代码 token 对选择知识迁移实例的价值。实验在 Samsung Kotlin 项目上评估修复性能。结果显示：基于开源和工业 Kotlin 数据集的迁移学习将缺陷修复性能提升 307%；迁移工业（非缺陷）bug 修复知识后，相较基线 DL-APR 提升 532%；利用嵌入向量与重叠 token 选择迁移实例最高提升 696%。
Deep learning-based automated program repair (DL-APR) can automatically fix software bugs and has received significant attention in the industry because of its potential to significantly reduce software development and maintenance costs. The Samsung mobile experience (MX) team is currently switching from Java to Kotlin projects. This study reviews the application of DL-APR, which automatically fixes defects that arise during this switching process; however, the shortage of Kotlin defect-fixing datasets in Samsung MX team precludes us from fully utilizing the power of deep learning. Therefore, strategies are needed to effectively reuse the pretrained DL-APR model. This demand can be met using the Kotlin defect-fixing datasets constructed from industrial and open-source repositories, and transfer learning. This study aims to validate the performance of the pretrained DL-APR model in fixing defects in the Samsung Kotlin projects, then improve its performance by applying transfer learning. We show that transfer learning with open source and industrial Kotlin defect-fixing datasets can improve the defect-fixing performance of the existing DL-APR by 307%. Furthermore, we confirmed that the performance was improved by 532% compared with the baseline DL-APR model as a result of transferring the knowledge of an industrial (non-defect) bug-fixing dataset. We also discovered that the embedded vectors and overlapping code tokens of the code-change pairs are valuable features for selecting useful knowledge transfer instances by improving the performance of APR models by up to 696%. Our study demonstrates the possibility of applying transfer learning to practitioners who review the application of DL-APR to industrial software.
DOI 原文 ·
@inproceedings{paperbot7098,
title = {An empirical study of deep transfer learning-based program repair for Kotlin projects},
author = {Misoo Kim and Youngkyoung Kim and Hohyeon Jeong and Jinseok Heo and Sungoh Kim and Hyunhee Chung and Eunseok Lee},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558967}
}